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How to compute the smallest safe deposit for a big order (integer cents, no AI)

Floating-point money is a classic bug source. Here is the small, deterministic calculation behind one of our free tools, the Deal Safety Calculator: the smallest deposit that keeps a business's cash above its reserve until a customer pays. The model Everything is kept in integer cents, and every cash movement is dated: cash0: cash today reserve: the floor the balance must not go below (payroll, rent) costs: what the business pays before delivery, with dates price: order value, paid on delivery + terms days other known outflows (payroll, rent) on their dates The lowest balance between today and the payment day decides whether the deal is safe. // all amounts are integers (cents); events are sorted by day
function lowestBalance(cash0, events) {
let bal = cash0, low = cash0;
for (const e of events) {
bal += e.amount; // negative = outflow
if (bal < low) low = bal;
}
return low;
}

// smallest deposit (in cents) that keeps the low point >= reserve
function minDeposit(cash0, reserve, events, price) {
const low = lowestBalance(cash0, events);
const gap = reserve - low; // how far below the floor we dip
if (gap <= 0) return 0; // already safe, no deposit needed
return Math.min(gap, price); // capped at the order value
}
The deposit arrives on day 0, so it lifts every later balance by the same amount. That is why the minimum safe deposit is simply the gap between the reserve and the lowest point, with no iteration and no guessing. Worked example A $40,000 order, $26,000 of costs paid before delivery, Net 60. With $30,000 in the bank, a $20,000 reserve and a $9,000 payroll in the gap, the low point is 30,000 − 26,000 − 9,000 = −5,000, so the gap is 25,000: a 62.5% deposit, or shorter terms that move the payment before payroll. Why not let an LLM do it? Ask a chatbot the same cash question twice and you can get two answers. Money decisions need the same answer every time, with the working shown. Our tools use fixed, tested formulas (integer cents, golden test vectors, boundary tests), and every result shows its working. Try the free calculator (no sign-up, runs in the browser): https://gettidycents.com/tools/deal-safety-calculator More tools for running a business: https://gettidycents.com/tools

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Fix html2canvas 'Attempting to parse an unsupported color function "oklch"' (Tailwind CSS v4)

When you call html2canvas() on a page that uses Tailwind CSS v4 — or any stylesheet that emits modern CSS color functions — the official html2canvas package may throw: Attempting to parse an unsupported color function "oklch"
The same parser also rejects related functions, so you may instead see "oklab", "lab", "lch", "color", or "color-mix" in the message. Why it happens html2canvas does not take a real screenshot. It walks the DOM, reads computed styles, and paints a canvas itself. That means it has to parse CSS color values. Tailwind CSS v4 uses the oklch() color space for its default palette. The browser is fine with that. Official html2canvas 1.4.1 (January 2022), by Niklas von Hertzen, was released before oklch() / oklab() showed up in everyday stylesheets, and its color parser throws on functions it does not know. So a page that looks correct in the browser can still fail at html2canvas(element) time. This is a parser limitation, not a Tailwind bug. Fixes that do not use html2canvas-lts Try these first. 1. Switch to html2canvas-pro html2canvas-pro is an independent maintained fork by Yorick Shan. It is built as a next-generation html2canvas and supports modern CSS colors. Verified on npm, 2026-11-16 (Asia/Shanghai): html2canvas-pro 2.6.4. npm uninstall html2canvas && npm install html2canvas-pro
import html2canvas from 'html2canvas-pro';
If this package already does what you need, use it. Thanks to Yorick Shan and contributors for maintaining it, and to Niklas von Hertzen for the original library. 2. Keep official html2canvas, but do not feed it oklch() styles If you must stay on html2canvas 1.4.1, the captured DOM cannot contain those color functions in computed styles. In practice that usually means: not using Tailwind v4's default oklch() palette on the nodes you capture, or snapshotting a clone whose colors have been rewritten to rgb() / hex first. This is often more work than changing the library, and it is easy to miss a theme token or a shadow. Prefer (1) unless you have a hard constraint on the official 1.4.1 package. A maintained html2canvas fork (optional) Another option is html2canvas-lts, a drop-in fork that teaches the original parser lab(), lch(), oklab(), oklch(), color(), and color-mix() instead of throwing. It is not the official html2canvas package, and it is a separate project from html2canvas-pro. Credit for html2canvas stays with Niklas von Hertzen and the original contributors; if the original project returns, the fork is meant to step aside. Verified on npm, 2026-11-16 (Asia/Shanghai): html2canvas-lts 1.5.4. The package README lists 37 fixes and no new runtime dependencies, including the color-function work. npm uninstall html2canvas && npm install html2canvas-lts
The API is the same; the module name changes: import html2canvas from 'html2canvas-lts';

html2canvas(document.querySelector('#capture')).then((canvas) => {
document.body.appendChild(canvas);
});
A script tag still exposes window.html2canvas:
The current fix list is on the project page: https://cyberpusa.org/forks/html2canvas/. The fork is kept by Cyber Pusa (赛博菩萨), an AI maintainer that adopts abandoned but still-used libraries. Issues can go to cyberpusa-ai/html2canvas. Which option to pick You want a modern, independently maintained renderer: try html2canvas-pro 2.6.4. You want to stay close to the original html2canvas API and pick up the oklch fix plus the other listed rendering fixes: html2canvas-lts 1.5.4. You cannot change libraries: keep 1.4.1 and make sure the captured tree does not compute to oklch() / oklab() / lab() / lch() / color() / color-mix(). In every case, the underlying library is Niklas von Hertzen's html2canvas. These packages exist because that work is still in use after the last official release.

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How to migrate a store off Shopify without losing SEO rankings

I've migrated a bunch of merchants off Shopify (I work on LaunchStore, an ecommerce platform — more on that at the end, but this guide is platform-agnostic). The part everyone fears isn't the products or the customers. It's the SEO. Years of Google equity live in URLs, and one careless cutover can erase it. Here's the sequence we run every time. It works no matter where you're moving. Step 0: Inventory your URLs before you touch anything Export every indexed URL. Three sources, unioned: Google Search Console → Performance → export queries and pages (last 16 months). yourstore.com/sitemap.xml (Shopify exposes product, collection, and page sitemaps). A crawl of the live site (Screaming Frog, or wget --spider -r if you're cheap like me). You should end up with a spreadsheet of every URL that (a) ranks, (b) has backlinks, or (c) gets traffic. Those are the URLs that must survive the move with a 301. Everything else is optional. Step 1: Map the URL structure Shopify's patterns are predictable: /products/{handle} → /products/{handle} (keep identical if you can) /collections/{handle} → /collections/{handle} (or /category/{handle}) /pages/{handle} → /p/{handle} (whatever your target uses) /blog/{handle} → /blog/{handle} The single biggest SEO win available to you: keep the same paths on the new platform. If your target platform lets you define product URLs, match Shopify's /products/{handle} exactly and most redirects become 1:1 no-ops. Every URL you don't have to rewrite is a redirect you can't get wrong. Where paths must change, build the mapping table now — one column old, one column new. This table is the migration. Step 2: Set up 301 redirects BEFORE DNS cutover The trap: people switch DNS first, then add redirects over the following week. Google re-crawls during that window, collects a pile of 404s, and you spend months recovering. Instead, on the new platform, configure the full redirect map from step 1 while the DNS still points at Shopify. Then test it: most platforms (ours included) let you preview the store on a temporary domain. Hit the temp domain with the OLD paths: while read url; do
code=$(curl -s -o /dev/null -w "%{http_code} %{redirect_url}" \
"https://temp-preview-domain.com${url#https://yourstore.com}")
echo "$url → $code"
done < url-list.txt
Every line should read 301 followed by the mapped URL you expect. Fix the ones that don't. This script has caught a wrong redirect on every single migration we've run — including our own demo store. Step 3: Move the content with its metadata Products: title, description, images (with filenames preserved — image search traffic is real), variants, and stock. Collections: keep the same handles. Blog posts: full HTML with internal links rewritten to the new URLs (internal links pointing at redirects is wasted crawl budget and diluted equity). One thing people forget: meta titles and descriptions. If the new platform regenerates them by template, you can lose the ranking even with a perfect redirect chain. Export them from Shopify (there's no native export — a Shopify app or an API pull GET /admin/api/products.json?fields=id,title,metafields gets you there) and re-apply them on the new platform. Step 4: DNS cutover, then watch three things Switch DNS. Keep the Shopify store on its plan for 48 more hours (you read that right — don't cancel yet; you may need to re-export something). After cutover, monitor: GSC Coverage report — new URLs should get discovered; old URLs should show "Page with redirect." That's the healthy pattern. Spikes in "Not found" mean your redirect map has holes. The queries report — rankings dip for 1–3 weeks on medium-competition terms. That's normal flux from a move. They recover if the redirects and content parity are right. Organic traffic daily, not weekly — you want to catch a cliff on day 2, not day 12. Step 5: Update the highest-value backlinks Redirects pass most equity, but not all. Sort your backlink profile (Search Console links report i...

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AgentLens AI agent audit trail: hash chaining, OpenTelemetry and MCP

An AI agent deleted a file, called a tool and burned through tokens. Six months later, who can prove that last night’s log was not edited? The AgentLens AI agent audit trail answers with a hash-chained journal. The agentkitai/agentlens repository is small and young. I cloned and read it without deploying it, so this piece separates what the code shows, what the README claims and what I did not check. The project is worth a look for one reason. Most agent observability tools measure. This one first wants to make history tamper-evident, then plug it into OpenTelemetry traces and the MCP protocol. We follow that thread: the journal, three ways in, the dashboard, a ten-minute trial, then the limits. Facts as of 9 November 2026. The repository is MIT-licensed (I read the LICENSE file, author Amit Paz). The 50 latest commits I fetched run from 2 July to 1 August 2026. I quote no star or download count: I could not read them on the repository page. AgentLens AI agent audit trail: a journal that checks itself The README calls AgentLens a flight recorder for agents. Each model call, tool call, approval decision and error becomes an event in an append-only journal. The core idea fits in one line: every event carries the SHA-256 hash of the one before it. The code in packages/core/src/hash.ts backs this up. The hash covers a format version, the identifier, timestamp, session, agent, event type, severity, payload, metadata and the previous hash. A session starts from null. To verify, you recompute each hash and compare the link with the previous event. In practice, editing a row, deleting one or swapping two breaks verification at the exact spot. Note that this is not a signature: someone who controls the whole database could recompute the entire chain. The project mentions a signed export for auditors (GET /api/audit/verify/export); I did not test it. Source : github.com The capture above reproduces the output the README gives for ./demo/aha.sh. The script ingests five events, verifies the chain, edits one record directly in the database, then verifies again. I did not run it: this is the output the project advertises. Three ways to feed the audit trail OpenTelemetry, no SDK If your agent already emits spans following the OpenTelemetry GenAI conventions, point the OTLP exporter at the server, which listens on port 3400: export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:3400
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:3400/v1/traces
According to the README, chat spans become call and response pairs, execute_tool becomes a tool call and invoke_agent an agent event. One trace maps to one session. Cost, which spans rarely carry, is rebuilt from a per-model price table: that is the project’s estimate, so check it against your invoice. In production, the OTLP_AUTH_TOKEN variable requires a token on these routes. The MCP server, on the agent side The @agentkitai/agentlens-mcp package goes into the agent’s MCP configuration, for instance Claude Desktop or Cursor. The docs/guide/mcp-integration.md guide describes a stdio transport and tools such as agentlens_session_start, agentlens_log_event and agentlens_log_llm_call. The README says 22 tools, the guide says more than 20: the difference in count is not explained. A reliability caveat applies. Here the agent itself decides to call the logging tool. An agent that forgets, or is hijacked by an injection, produces an incomplete journal. For a robust audit, prefer OpenTelemetry instrumentation that lives outside the agent. MCP tool risks are covered in our article on MCP tool poisoning. The Python and TypeScript SDKs To read sessions and analytics, pip install agentlensai or npm install @agentkitai/agentlens-sdk give you a client with an API key. The README also lists automatic Python instrumentation for several model providers, with a redaction option. What the dashboard shows The session detail view shows a timeline: tool calls, an error, tracked cost, session end. A Chain Va...

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Gravity In A Bottle: NASA SPACE APPS

This is a submission for the MLH x DEV Writing Challenge What I Built Gravity in a Bottle is an interactive, retro-styled space-archaeology and narrative simulation game set aboard the stranded deep-space vessel Odyssey-6. After 19 days of radio silence from Earth, the ship's six-person crew turns to the onboard Archive Terminal to study how historic robotic spacecraft survived extreme planetary environments and communicated across millions of kilometers—hoping to reverse-engineer a way to bring their own backup dish online. How It Works Interactive Solar System Orrery & Planetary Dossiers: Explorers navigate an orbital telemetry radar covering 10 celestial bodies—Mercury, Venus, Earth, the Moon, Mars, Jupiter, Saturn, Titan, Uranus, and Neptune. Each dossier pairs planetary science with real archival records and photography of the robotic pioneers humanity left behind on those worlds. 7 Multi-Stage Historical Field Simulations: Players pilot seven real spacecraft across 3-stage simulations grounded in actual planetary physics and failure modes: MESSENGER (Mercury): Lock a ceramic-cloth sunshade against 430 °C solar glare, ping shadowed polar ice craters, and execute its 2015 Shakespeare Crater impact. Venera 13 (Venus): Survive 92-bar crushing pressure and 465 °C heat to drill Phoebe Regio and beam back the first color panorama of Venus. Lunokhod 1 & Surveyor 3 (The Moon): Drive the 8-wheeled Soviet rover across Mare Imbrium with a 2.6-second radio delay, and operate Surveyor 3's soil sampler claw in Oceanus Procellarum ahead of the Apollo 12 rendezvous. Ingenuity (Mars): Spin counter-rotating carbon blades at 2,537 RPM in air 1% as dense as Earth's while battling shifting Martian winds in Jezero Crater. Galileo Atmospheric Probe (Jupiter): Plunge into Jupiter at 170,000 km/h, match carrier transponder frequencies inside the Orbiter relay beam, and survive up to 22-bar pressure. Huygens Lander (Titan): Descend through Titan's orange nitrogen-methane haze, deploy the main canopy below 130 km, and touch down on Xanadu. Odyssey-6 Archive AI Terminal: Built right into the radar view, an in-character Q&A terminal lets players ask open-ended questions strictly focused on lost, abandoned, and derelict instruments in space. Home Is Calling: Completing at least four field simulations unlocks Earth's orbital link and triggers the crew's final transmission home, culminating in Mission Control's reply: "We've always been listening." What Inspired It & What I Learned When we talk about space exploration, we usually celebrate launches and first landings—rarely the silent hardware left behind as permanent monuments to human curiosity. Building this taught us how to translate real engineering constraints (like parachute shred altitudes, thermal runaway, and radio latency) into intuitive 2D Canvas mechanics, while using procedural Web Audio synthesis and pixel-art CRT framing to make an educational archive feel like a cohesive 16-bit sci-fi game. Demo Live Deployed App: Play Source Code: Github Partner Technologies Google Gemini API (@google/genai — gemini-2.5-flash) We integrated the Google Gemini API to power the Odyssey-6 Archive Terminal, a collapsible Q&A assistant embedded directly inside the Solar System Radar scene. Serverless Architecture: We built a dedicated Next.js App Router endpoint (/api/archive-bot) using the official @google/genai SDK (v1beta) so API credentials remain secure on the server while keeping multi-turn conversation history fast and lightweight. Strict In-Character Domain Guardrails: Using systemInstruction, we engineered the model to act as an onboard spacecraft archive computer that only answers questions about lost, abandoned, decommissioned, or crashed space hardware (including spaceflight acronyms like LRV, LM, and ALSEP), while politely refusing off-topic prompts in character ("ARCHIVE ACCESS DENIED: Query outside mission parameters..."). Resilience & UX: Because live hackathon demos can face spotty netwo...

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Inside the AWS Default VPC (and Why You'll Want Your Own)

Introduction: A guided tour of the default VPC that comes with every AWS account: subnets, route table, internet gateway, NACL and security group. Then how to create your own VPC. Every AWS account comes with a network you never asked for. It's already there, already wired to the internet, and it's the reason your first EC2 instance "just worked." It's called the default VPC. Before building a VPC from scratch, it helps to know what AWS builds for you. So this article is a tour: first the default VPC and everything attached to it, then how to create your own. By the end, you'll know what each piece does and how they connect. What's a VPC? A Virtual Private Cloud (VPC) is your own isolated network inside AWS. You choose its IP range, split it into subnets, and control what can talk to what. A VPC lives in one region, and mine is in Europe (Stockholm) eu-north-1. Coming from Azure? A VPC is the equivalent of a VNet, and both live in a single region. The difference is the subnet: in AWS, a subnet lives in one Availability Zone, so the subnet you pick decides which zone your resource runs in. In Azure, a subnet spans the whole region and you choose the zone per resource. Meet the default VPC In the console, search for VPC and open Your VPCs. My account lists exactly one, and the Default VPC column says Yes. A few things stand out: IPv4 CIDR 172.31.0.0/16: the range of private IP addresses this network can hand out. The /16 means 65,536 addresses. Tenancy: default: your resources run on shared AWS hardware, which is what you want in almost every case. Main route table and Main network ACL: two IDs we'll visit shortly. Scroll the table to the right and you'll find the VPC ID, the State (Available) and Block Public Access (Off). Select the row and a details panel opens at the bottom. DNS resolution and DNS hostnames are both Enabled, which is why instances in this VPC get DNS names out of the box. For the big picture, click the VPC ID and open the Resource map tab. It shows 3 subnets, 1 route table and 1 network connection, which is an internet gateway. Everything below is just us zooming into this map. Subnets: one per Availability Zone Open Subnets in the left menu. There are three, one per Availability Zone, all Available, with ranges 172.31.0.0/20, 172.31.16.0/20 and 172.31.32.0/20. Each /20 holds 4,096 addresses. Open one and check its details: Availability Zone: eu-north-1b. The zone name is mapped per account, so the AZ ID (eun1-az2) is the one that always points to the same physical zone. Available IPv4 addresses: 4091. AWS reserves 5 addresses in every subnet. Default subnet: Yes and Auto-assign public IPv4 address: Yes. That last setting is why default subnets are "public": anything you launch here gets a public IP automatically. The route table: how traffic finds its way Still inside the subnet, click the Route table tab. There are only two routes: 172.31.0.0/16 → local: traffic inside the VPC stays inside the VPC. 0.0.0.0/0 → igw-...: everything else goes to the internet gateway. That second route is what makes a subnet public. Open Route tables in the left menu to see the table itself. It's marked Main: Yes, and it has no explicit subnet associations. Subnets without an explicit association automatically use the main route table. The internet gateway: the door Under Internet gateways, you'll find one, with its state set to Attached to our VPC. Think of it this way: the gateway is the door, and the route is the sign pointing to it. Without both, nothing reaches the internet. Two layers of firewall: NACL and security group Back on the subnet, the Network ACL tab says this subnet uses the VPC's default network ACL. Its inbound and outbound rules are the same: rule 100 allows all traffic, and rule * denies everything else. Rules are checked from the lowest number up, so the allow always wins and the deny never triggers. The NACL's own page shows it's associated with all 3 subnets and is the default one. Then there's the ...

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Cómo saber si un proceso mejoró de verdad (y qué indicadores lo demuestran)

Un proceso mejoró si el mismo indicador, medido igual antes y después, se movió en la dirección que buscabas y se sostiene varias semanas sin que nadie esté empujando. Los cuatro que sirven en una pyme: tiempo de ciclo, errores o retrabajo, pasos manuales por caso y costo por transacción. Sin línea base no hay mejora: hay sensación. Lo escribo desde el lado de quien instala el cambio. En Árkos la pregunta me llega casi siempre tarde: el sistema ya está andando, el equipo dice que «ahora es más rápido» y nadie puede demostrarlo, porque no se anotó cómo era antes. Esta guía es el método que uso para que eso no pase, y para distinguir una mejora real de una que solo se siente. ¿Qué significa que un proceso mejoró? Significa tres cosas a la vez, y las tres tienen que cumplirse: Un indicador declarado antes del cambio se movió. No «el que mejor quedó» al final: el que dijiste que ibas a mirar. El resto no empeoró. Si el pedido sale 30% más rápido pero las devoluciones subieron, no mejoró el proceso: se trasladó el costo. Se sostiene sin empujón. La semana del lanzamiento todo el mundo hace bien su trabajo porque está siendo mirado. La pregunta es qué pasa a las seis semanas, cuando ya nadie recuerda que hubo un cambio. Lo que no cuenta como mejora: que la herramienta esté instalada, que el equipo diga en una reunión que le gusta, o que el proveedor muestre un tablero con gráficos. Eso es actividad, no resultado. ¿Qué indicadores miden la mejora de un proceso? Cuatro, y casi siempre basta con dos. Los elijo por una razón práctica: se pueden sacar de registros que ya existen, sin montar nada nuevo. Tiempo de ciclo Cuánto pasa desde que el trabajo entra hasta que sale terminado. No el tiempo que alguien «le dedica», sino el reloj de pared: del pedido al despacho, de la orden abierta a la orden cerrada, de la consulta a la respuesta. Es el indicador que más le importa al cliente, porque es el único que él nota. Mídelo con la mediana, no con el promedio. Un caso raro de tres semanas te mueve el promedio y te hace creer que todo va mal; la mediana te dice cómo es el caso típico. Y mira además el percentil 90: ahí viven los clientes que se quejan. Errores y retrabajo Cuántas veces hay que volver a hacer algo: una factura reemitida, un pedido corregido, una cita reagendada, un dato que alguien vuelve a escribir porque llegó mal. Es el indicador más fácil de ignorar y el que mejor explica por qué el equipo vive apagando incendios. Cuéntalo como porcentaje de casos, no en números absolutos: si el volumen creció, 20 errores al mes pueden ser una mejora frente a 15. Pasos manuales por caso Cuántas intervenciones humanas necesita un caso para completarse. Se cuenta a mano, una vez, siguiendo tres o cuatro casos reales de principio a fin y anotando cada vez que una persona abre algo, copia algo, avisa a alguien o espera una respuesta. Es el indicador que predice mejor si automatizar va a servir, porque cada paso manual es un lugar donde el proceso puede pararse. Costo por transacción Las horas que consume un caso, multiplicadas por el costo hora de quien lo hace, más lo que cuestan los errores. Es la traducción a soles de los tres anteriores y la única cifra que sirve para decidir cuánto vale la pena invertir en arreglarlo. Hay un quinto que no es indicador de proceso pero conviene tener al lado: el volumen. Casi todo lo demás se interpreta mal si no sabes si entró más o menos trabajo que antes. ¿Cómo tomo la línea base si nunca medí nada? Es la situación normal, no la excepción. En el Perú, según la Encuesta Económica Anual 2024 publicada por el INEI, en 2023 solo el 9,6% de las empresas con ventas desde 50 UIT usaba un software ERP, el 4,8% un CRM y el 1,6% un SCM. Nueve de cada diez empresas no tienen un sistema que registre el proceso, así que la línea base no está esperando en una base de datos: hay que construirla. Tres formas, de la mejor a la peor, y todas valen más que ninguna: Reconstruirla de lo que ya existe. Los cuadernos, el Ex...

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Your CSP blocks eval. So I wrote a parser.

I needed a graphing calculator that plots sin(2x) + x^2 while you type. The obvious implementation is one line: const y = eval(expression.replace(/x/g, String(x)));
It is also the one line that cannot ship, because the page sends this header: Content-Security-Policy: script-src 'self'; object-src 'none';
No 'unsafe-eval', so eval and new Function throw. That is not an accident I wanted to work around — it is the reason the site has no XSS surface from user input. The expression parser had to be real, so I wrote one. It is about eighty lines. This is how it works, and the three places where a naive implementation breaks. The pipeline Four stages, each one testable on its own: "sin(2x) + x^2" → tokens → RPN queue → evaluate(x)
If you have only ever written a calculator with eval, the surprise is that the hard part is not the arithmetic. It is deciding what -3^2 means and whether 2x is a multiplication. Stage 1 — tokenize A tokenizer turns a string into {type, value} objects. Numbers, identifiers (variables and function names), operators, parentheses, commas. const OPS = "+-*/^";
const isDigit = (c) => c >= "0" && c <= "9";
const isAlpha = (c) => /[a-zA-Z]/.test(c);

function tokenize(src) {
const out = [];
let i = 0;
while (i < src.length) {
const c = src[i];

if (c === " ") { i++; continue; }

if (isDigit(c) || (c === "." && isDigit(src[i + 1]))) {
  let j = i;
  while (j < src.length && (isDigit(src[j]) || src[j] === ".")) j++;
  // exponent form: 1e-3
  if (src[j] === "e" || src[j] === "E") {
    let k = j + 1;
    if (src[k] === "+" || src[k] === "-") k++;
    if (isDigit(src[k])) {
      while (k < src.length && isDigit(src[k])) k++;
      j = k;
    }
  }
  out.push({ type: "num", value: Number(src.slice(i, j)) });
  i = j;
  continue;
}

if (isAlpha(c)) {
  let j = i;
  while (j < src.length && (isAlpha(src[j]) || isDigit(src[j]))) j++;
  out.push({ type: "name", value: src.slice(i, j) });
  i = j;
  continue;
}

if (OPS.includes(c) || c === "(" || c === ")" || c === ",") {
  out.push({ type: c });
  i++;
  continue;
}

throw new Error(`Unexpected character: ${c}`);

}
return out;
}
Note 1e-3. Without the exponent branch, 1e-3 tokenizes as 1, e, -, 3 — and e is not a variable you defined, so the error message is confusing. Stage 2 — shunting-yard Dijkstra's algorithm, and for an expression parser it is still the shortest correct path: push numbers straight to the output queue, push operators onto a stack, pop according to precedence. const PREC = { "+": 1, "-": 1, "*": 2, "/": 2, "^": 3 };
const RIGHT = new Set(["^"]); // right-associative
const FUNCS = new Set(["sin", "cos", "tan", "log", "ln", "sqrt", "abs", "exp"]);

function toRPN(tokens) {
const out = [];
const stack = [];
let prev = null;

for (const t of tokens) {
if (t.type === "num") { out.push(t); }
else if (t.type === "name") {
if (FUNCS.has(t.value)) stack.push({ type: "func", value: t.value });
else out.push({ type: "var", value: t.value });
}
else if (t.type === "(") { stack.push(t); }
else if (t.type === ")") {
while (stack.length && stack[stack.length - 1].type !== "(") out.push(stack.pop());
if (!stack.length) throw new Error("Unbalanced )");
stack.pop();
if (stack.length && stack[stack.length - 1].type === "func") out.push(stack.pop());
}
else if (t.type === ",") {
while (stack.length && stack[stack.length - 1].type !== "(") out.push(stack.pop());
}
else {
// unary minus: only when nothing can precede it
const unary = (t.type === "-" || t.type === "+") &&
(!prev || prev.type === "(" || prev.type === "," ||
(prev.type !== "num" && prev.type !== "var" && prev.type !== ")"));
if (unary) {
if (t.type === "-") stack.push({ type: "neg", value: "-" });
...

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Power BI for Sports Performance Analytics: Turning Athlete Data into Better Results

In modern sports, talent and hard work are important, but understanding performance through data can make a significant difference. Professional teams, coaches, and fitness professionals increasingly rely on analytics to evaluate athletes, improve training strategies, and monitor physical health. Every training session and competition generates valuable information, including speed, stamina, workload, recovery time, and physical performance. However, managing all this information manually can make it difficult to identify patterns and measure progress. This is where Power BI for sports performance analytics becomes useful. Microsoft Power BI helps sports organizations transform raw athlete data into interactive dashboards, making it easier to understand performance and make informed training decisions. Why Sports Teams Need Data Analytics ? Sports performance depends on several interconnected factors. An athlete may perform exceptionally well during one competition but experience fatigue or reduced performance in the following sessions. Coaches need a clear understanding of these patterns to make better decisions about training intensity, recovery, and athlete development. Power BI dashboards can help sports teams: Monitor individual athlete performance. Track training progress over time. Analyse workload and physical activity. Identify recurring injury patterns. Monitor health and recovery indicators. Compare performance across athletes and training sessions. By organizing these metrics in one place, coaches and analysts can spend less time preparing reports and more time understanding performance. Important Sports Performance Metrics to Track Using Power BI An effective sports analytics dashboard should focus on metrics that provide meaningful insights into athlete performance and physical condition. 1. Athlete Performance Tracking Performance tracking helps coaches understand how athletes are progressing throughout their training programmes. Depending on the sport and available data, important indicators may include: Running speed and sprint times Endurance and stamina Training workload Strength and physical performance Session-wise performance improvements Competition performance trends Visualizing these metrics over time helps coaches understand whether training programmes are delivering the expected results. 2. Injury Analysis and Monitoring Injuries can affect an athlete's development, training schedule, and participation in competitions. Sports organizations can use Power BI to organize injury-related information, including injury frequency, injury types, affected body areas, recovery timelines, and previous injury records. Analysing historical injury data can help sports professionals identify recurring patterns and discuss appropriate preventive strategies. It is important to remember that dashboards support monitoring and analysis; medical diagnosis and return-to-play decisions should remain with qualified healthcare professionals. 3. Athlete Health Monitoring An athlete's physical condition plays an important role in overall performance. Depending on the available data and monitoring equipment, health dashboards can bring together indicators such as heart rate, sleep duration, wellness scores, and training-related physical measurements. Having this information in a centralized report can help coaches and support staff understand changes in an athlete's condition and coordinate appropriate follow-up. 4. Recovery and Fatigue Tracking Training is only one part of athletic development. Recovery is equally important. Athletes need sufficient rest between intense training sessions to maintain performance and manage physical stress. A recovery analytics dashboard can help track: Rest and recovery periods Training frequency Workload changes Athlete-reported fatigue Sleep and wellness indicators Recovery trends over time These insights can support more informed discussions about training intensity and recovery planning....

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"infinite recursion detected in policy for relation" in a Supabase app

You added sharing, or teams, or an admin role. Now a page that worked yesterday loads nothing at all, and the console shows this: infinite recursion detected in policy for relation "profiles"
with the code 42P17. Nothing is corrupted and no data is lost, but as Supabase's own documentation puts it, "the query fails for every role the policies apply to", so this is not one unlucky user. One of your row level security policies asks the database a question it cannot finish answering, and PostgreSQL stops it rather than looping forever. What the database is actually complaining about A policy is a WHERE clause the database adds to every query on that table. So when a policy on profiles contains a subquery that reads profiles, the database has to apply the policy to that subquery too, which contains a subquery that reads profiles, and so on. PostgreSQL spots the loop while it is still planning the query and refuses. This is the shape almost everyone writes. You want admins to see every row, so you check whether the current user is an admin, and the only place that is recorded is the table you are protecting: -- Reject: this policy on profiles reads profiles.
create policy "admins read all" on profiles for select
to authenticated
using (
exists (
select 1 from profiles p
where p.id = (select auth.uid()) and p.role = 'admin'
)
);
The second shape is two tables that each check the other. It appears the moment you add sharing: a policy on projects reads members to see who it is shared with, and the policy on members reads projects to see who owns it. Neither can resolve without the other. Three things about this error that waste people's time I ran all of this against a real PostgreSQL 16.15 server to see what it does, rather than describing it from memory. Three results are worth knowing before you start editing policies. 1. The table named in the error is the table you queried, not the broken one. With a projects and members cycle, selecting from projects says relation "projects" and selecting from members says relation "members". Same cycle, two different messages. If you go straight to the table in the message and rewrite its policy, you may be editing the innocent one. 2. It fails while planning, so even explain fails. explain select * from profiles gives the same error. That is a quick way to confirm you have found the right table without touching any data. 3. It may well not reproduce in the SQL Editor. A table's owner bypasses row level security entirely, and I confirmed that: the same select that errors for anon returned every row for the owner, with no error at all. The SQL Editor connects with a privileged role rather than your app's anon or authenticated, so "but it works in the SQL editor" tells you nothing either way. Take on your app's role before you test, the way Supabase's own policy tests do: begin;
set local role authenticated;
set local request.jwt.claim.sub = '';
select * from profiles; -- now you should see the error
rollback;
Find the culprit in about a minute Run this to list every policy and the expression inside it. The using_expression column is where you are looking for the name of the table the policy is attached to, or the name of another table whose policy points back. select tablename, policyname, cmd,
qual as using_expression,
with_check
from pg_policies
where schemaname = 'public'
order by tablename, policyname;
Read down the list for any policy whose expression mentions its own table, and any pair of tables that mention each other. That pair is your cycle. The fix: read the membership table as its owner The documented way out is a security definer function. It runs as the role that created it, which on Supabase is postgres, and postgres has bypassrls. So the function reads the membership table without any policy being applied, the cycle never forms, and the policy gets a plain list of ids back. For the admin case above: create s...

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Testing what users see when an AI safety check fails

When we built Koda, an app that teaches kids how AI works, we had a question that a normal answer-quality eval couldn't settle. If the model has a draft ready and the safety check fails, what actually reaches the screen? We built an offline test around that boundary. The application holds the draft while checking it alongside the original question. The gateway rechecks permission before it delivers an approved answer. If the detector is unavailable, the application can display a predefined message without showing the draft. Reproduce it locally With Node.js 22 or later, run: git clone https://github.com/SamSquareAI/child-ai-safety.git
cd child-ai-safety
npm ci --ignore-scripts
npm run --silent demo
The demo uses invented prompts and simulated providers. It runs without API keys. The terminal shows four paths: an approved answer, a refusal, a support response, and a simulated detector outage. In the outage case, the learner-facing message says Koda can't answer right now and suggests trying a learning question later. What to check in your own app Run the test through the same delivery path your UI uses. Make the safety adapter return unavailable after a draft exists. Assert that the application renders the fallback and that no part of the draft was emitted. In a streaming app, that means checking the entire event sequence, including chunks sent before the final message. Test an ordinary approved answer too, so you know both paths work. I help maintain Child AI Safety, the Apache-2.0 developer preview that came out of this work. If you'd like a small first contribution, issue #8 asks for a runnable example showing how the gateway counts approved and unavailable requests locally. Drafted with AI and checked against the repository's offline demo.

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I built an MCP server that lets any AI agent read the PDFs and spreadsheets in your S3 bucket

AI agents are good with code because code is plain text on a local disk. The agent looks at what's there, searches, and opens only the part that matters. A company's knowledge doesn't look like that. It's annual reports, spreadsheets and contracts in an S3 or R2 bucket. An agent with a shell downloads them and writes Python to parse them, again on every run. An agent without a shell (Claude Desktop, a chat app, a support bot, any MCP-only client) often can't use them at all. That's the first half of the video. I gave Claude Code (Sonnet) only its Read, Grep and Glob tools, put the World Bank's 82 MB World Development Indicators workbook in the folder, and asked for one number: > Bhutan: access to electricity in 2005?

● Glob **/WDIEXCEL.xlsx
- WDIEXCEL.xlsx
● Read WDIEXCEL.xlsx
✗ This tool cannot read binary files. The file ap…

ANSWER: Unable to determine. I couldn't read the xlsx…
11.8 s · $0.010
So I built bucketlens, an open source MCP server that lets any agent read the PDFs, spreadsheets and CSVs in your own bucket. Same question, with no files on disk and no shell, only bucketlens: ● outline real/wdi/WDIEXCEL.xlsx
- real/wdi/WDIEXCEL.xlsx: 6 table(s)
● query SELECT "Country Name","Indicator Name","2…
- | Bhutan | Access to electricity (% of populati…

ANSWER: 59.8% of Bhutan's population had access to el…
13.9 s · $0.023
(Lines are cut at the terminal width, as in the video. The value in the file is 59.8.) What the agent gets Seven tools, built around how a person reads a long document: get the shape first, then go to the part that matters. Tool What it returns list_files(prefix) names, sizes, dates; nothing is downloaded outline(path, depth) PDF: page count, table of contents with page numbers, what reading it whole would cost in tokens. Spreadsheet: sheets, rows, columns and types search(path, text) the pages of one PDF that contain the text, with snippets search_all(text, folder) every PDF in a folder at once, as hits or just file names and counts read_pages(path, pages) the text of specific pages, like "36-38" query(path, sql) read-only SQL over a spreadsheet or CSV; each sheet is a table read_rows(path, sheet, first, last) rows by the spreadsheet's own row numbers Parse once, keep it next to the file My first idea was to skip parsing and read slices of the original file with HTTP range requests. I measured it. Opening a PDF or reading the first rows of a workbook fetched 5 to 7% of the file, which is fine. Deep pages and deep rows fetched 50 to 75%, because of how PDF and XLSX are laid out inside. That's barely better than downloading. So bucketlens reads each file once, the first time it's touched, and stores the result in your bucket next to it: .bucketlens/index///
manifest.json outline, sheets, and where each page starts
pages.txt PDF: every page's text, back to back
sheets/.parquet XLSX/CSV: one Parquet file per sheet, with a _row column
PDF pages are byte ranges in pages.txt, so reading pages 600 to 602 of a 733-page PDF is one range request of about 150 KB. Sheets are queried with DuckDB straight from the bucket. It reads only the columns and row groups a query needs: rows 150,000 to 150,049 of a 20 MB workbook came to about 190 KB. The ETag is in the path. When a file changes, its ETag changes, so an agent is never answered from an old version. manifest.json is written last, so a half-built index is never used. Because the index lives in the bucket and not on one machine, every agent, sandbox and laptop pointed at the bucket shares it. Keeping the index fresh You can run bucketlens index once, but files change. On Cloudflare R2, bucket event notifications go to a Queue, and bucketlens watch pulls from it over HTTP. In my tests a new 492-page PDF was indexed 8.8 s after its upload started, an edited workbook 4.4 s after it was saved, and a deleted file's index was gone 3 s later. For any other S3-compatible storage, bucketlens watch --poll 300 lis...

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Finding Real Opportunities When Everything Looks Like Noise

Finding Real Opportunities When Everything Looks Like Noise The Problem in Concrete Terms You have capacity—time, capital, skills, or a team—but you cannot decide where to point it. Every direction looks either saturated, risky, or vague. You read about "blue oceans" and "unmet needs," but when you sit down to list them, you produce either obvious ideas (Uber for X) or problems too small to sustain a business. You are not lacking creativity; you are lacking a reliable filter. The result is analysis paralysis: you research markets you will never enter, interview users you will never serve, and build prototypes for problems that do not exist. Meanwhile, the window to act keeps shrinking. Why the Usual Fixes Fail Trend reports and market maps tell you where the crowd was six months ago. By the time a sector appears on a Gartner quadrant or a VC thesis deck, the asymmetric upside is gone. You are buying retail. Keyword research and SEO tools surface demand that is already articulated. If people are searching for it, a solution likely exists. True opportunity lives in the gap between what people do and what they say—the workaround, the spreadsheet hack, the manual process they complain about in private Slack channels but never type into Google. Brainstorming frameworks (SCAMPER, Jobs-to-be-Done canvases, Design Thinking workshops) generate volume, not signal. They produce 50 ideas you cannot validate and one you fall in love with because it’s yours, not because it’s viable. They lack a kill criterion. Competitor analysis leads to feature parity wars. You end up building the 12th project management tool with "better UX" instead of discovering the workflow nobody has automated because it crosses departmental boundaries and looks messy. Step-by-Step Solution 1. Map Your Asymmetric Advantages (30 Minutes) Opportunity is not objective; it is relative to you. List every asset you control that is hard for others to replicate. Be ruthlessly specific. Access: Who trusts you enough to reply to a cold email in 5 minutes? Which communities (Discords, Slacks, industry associations, alumni groups) do you have admin/mod status or deep reputation in? Data: What proprietary datasets do you own or have legal access to? (Support tickets, sensor logs, anonymized transaction histories, regulatory filings you know how to parse). Skill/Tech: What can you build in a weekend that takes a typical team a month? (Specific legacy stack migration, niche hardware integration, regulatory compliance automation, computer vision on a specific defect type). Constraint Knowledge: What regulations, physics limits, or organizational politics do you understand viscerally because you’ve been burned by them? Output: A one-page "Unfair Advantage Inventory." If an opportunity does not touch at least two items on this list, discard it. This is your primary filter. 2. Hunt for "Expensive Workarounds" (2 Weeks, Ongoing) Stop looking for "problems." Look for expensive, recurring, manual workarounds performed by professionals who have budget authority. These are the only opportunities worth pursuing. Where to look (do not cold email): Your Unfair Advantage Inventory contacts: Message 10–15 people you actually know. Ask: "What is the most annoying spreadsheet/process you maintain manually because no tool handles [specific constraint]?" Do not pitch. Just listen. Public procurement / RFPs: Search government and enterprise tender sites (SAM.gov, Tenders Electronic Daily, state portals) for keywords from your inventory. The requirements section is a spec sheet for an unmet need. The winner often builds custom software; you can productize the 80% common core. Specialized job boards: Look for roles like "Clinical Data Coordinator," "Trade Compliance Analyst," "Estimator," "QA Technician." Read the responsibilities section. Every "manually reconcile," "copy-paste between systems," or "maintain tracker" is a product spec. Niche forums/Reddit/LinkedIn Groups: Search for "[Role] + spre...

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Your closing modal is still eating clicks

You close a modal. The exit animation plays for 200ms. During those 200ms the user clicks the button behind it. Nothing happens. The modal is fading out, so it looks gone. But it is still in the DOM, still on top, still catching the click. This is one of the most common bugs in animated overlays, and almost every Framer Motion modal tutorial has it. Why it happens AnimatePresence keeps a child mounted after you remove it, so it can play the exit animation. That is the whole point. But "mounted" means the full overlay: the backdrop, the panel, the focus trap, everything. So for the length of the exit: the backdrop still covers the page and swallows pointer events the dialog is still in the tab order, so focus can land in it screen readers still see a dialog that is already closing Most code gates interaction on open:
{open && (

)}

That check never works. When open is false, this element is not rendered with new props anymore. AnimatePresence keeps the last render, the one where open was true, and animates it out. The pointerEvents: "auto" is frozen in. The boolean that knows Framer Motion already tracks this. useIsPresent() returns false from the moment a child starts its exit. The catch: it only works inside the AnimatePresence subtree, one component below it. So read it in a small child component, and pass it down: import { useIsPresent } from "motion/react";
import type { ReactNode } from "react";

export function PresenceGate({
children,
}: {
children: (gate: { inert: boolean; style: { pointerEvents: "auto" | "none" } }) => ReactNode;
}) {
const isPresent = useIsPresent();
return children({
inert: !isPresent,
style: { pointerEvents: isPresent ? "auto" : "none" },
});
}
Then spread the gate onto every layer that takes pointer events:
{open && (

{(gate) => (
<>


...


)}

)}

Why inert, not just pointer-events pointer-events: none fixes the mouse. It does nothing for the keyboard or screen readers. inert does all three in one attribute: no pointer events, out of the tab order, hidden from the accessibility tree. An exiting dialog is not a dialog anyone should still be able to type into. Both flip in the same commit that starts the exit. The visual exit keeps playing. The page behind is usable instantly. The rule Gate interaction on presence, not on open. open tells you what the user wants. Presence tells you what is actually on screen and still in charge. During an exit those two disagree, and the page should follow the user. Every overlay in beUI works this way: the command palette, bottom sheet and image viewer all release the page the moment they start closing. Install any of them with the shadcn CLI and the gate comes with it: npx shadcn@latest add @beui/bottom-sheet

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Coding Agent Index v2.1: scores fell 5 points, 4 configs tie at 58, and cost per task spans 30x

On November 14 Artificial Analysis shipped version 2.1 of its Coding Agent Index, the leaderboard that scores a coding agent as one unit of harness plus model plus effort setting. The short version: nearly everyone lost points, the lead is a four-way tie at 58, and the cost to run one task at the top of the board spans roughly 30x. When the index reset in October I wrote about what the v2.0 reset measures. The v2.1 change is narrower but sharper, and the cost column is the part worth your time if you pick models for a coding agent. What v2.1 changed The index is an equal-weight average of three benchmarks, 322 tasks total, three attempts per task: Component Tasks What it measures How it scores FrontierCode-AA 2.0 132 changes to real open-source repositories maintainer-merge rubric, 0 or 70-100% Terminal-Bench 4.1 66 multi-step terminal work test suite, pass/fail SWE-Atlas-QnA-AA 124 repository question answering binary pass/fail FrontierCode-AA 2.0 is Artificial Analysis' implementation of Cognition's FrontierCode 2.0, released October 27. An attempt that fails any blocking check scores zero. An attempt that passes them all scores between 70% and 100% depending on non-blocking checks. A trajectory scanner zeroes any attempt that went looking for the upstream solution or its merge commit. Terminal-Bench 4.1 keeps 4.0's 66 tasks and environments and replaces the verifiers. Code that runs during grading now runs as an untrusted user in a separate process, and verifiers that rejected valid solutions were corrected. That is reward-hacking hardening, not new tasks. The third component and the weighting did not change. The largest component got harder, and that moved the whole board. The full methodology page is worth a read. All 23 dual-measured configs lost points on the new FrontierCode Per the v2.1 announcement, index scores came down about 5 points on average, driven by the FrontierCode swap. AlphaSignal's read of the underlying data is more specific: all 23 configurations evaluated on both versions scored lower on the new FrontierCode, and the mean FrontierCode score fell 16 points, from 45% to 29%. That is not the models getting worse. FrontierCode 2.0 pulls from 55% more repositories, carries 43% more blocking criteria per task, and its reference solutions modify 67% more files and 89% more lines in codebases that are 25% larger. The share of thoroughness criteria roughly tripled. Thoroughness criteria catch the patch that fixes the reported case and misses the sibling code path, and Cognition reports thoroughness produced the highest blocker failure rate at every reasoning level they analyzed. One configuration barely moved: Codex with GPT-6.1 Terra at max effort stayed within a point of its previous index score. Everyone else ate the reset. This is the second consecutive version change that lowered scores across the board. That is the design. A suite that refreshes before vendors tune against it keeps some discriminating power. A score you saw quoted last month is a different exam. The lead at 58 is two models in four configurations The top score belongs to four configurations, not one product: Claude Code with Fable 5.2 at high effort, Claude Code with Fable 5.2 at xhigh, Claude Code with Opus 5.5 at max, and Devin Fusion CLI with Opus 5.5 Max paired with GPT-6 Luna High. Two details matter more than the rank. First, Fable 5.2 at max scores 56, below its own high-effort result, because more of its attempts hit task time limits. Effort is not a simple dial where more is better. Second, Gemini 4 Carbon at high effort scores 55 in Antigravity CLI. The index deliberately scores the harness and the model together, which is why I trust it more than raw model benchmarks for agent work. We have seen this before from the other side: Hugging Face measured the same model scoring 33% or 62% depending on the harness. The 30x cost spread is the actionable column v2.1 also changed how cost is computed. Artificial Analysis aligned it ...

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Gemini CLI v0.68 fixes rootless Podman sandboxing and path boundary checks

Originally published at https://aicoding-guide.com. Gemini CLI v0.68.0 ships with no summary prose at all — the release page is just a list of pull requests. One feature; everything else is a fix. It looks unremarkable, but three of the fixes are about boundaries, and each one is a case where behaviour the docs already describe was not holding in some environment. Key point What you will learn When --userns=keep-id is passed for rootless Podman, and how to override it The fixes to root checks on Windows and to symlink resolution in GlobTool That the new extension validation command and the hooks' SubagentStop are both absent from the docs --userns=keep-id reaches rootless Podman The fix is fix(sandbox): supply --userns=keep-id for rootless podman (#30118). This behaviour is documented, under "Linux UID/GID handling": Gemini CLI automatically handles user permissions in container sandboxes on Linux. When the sandbox runs as your host UID/GID (the default on supported Linux distributions) and you use rootless Podman, Gemini CLI also passes --userns=keep-id. This keeps files that the sandbox creates in mounted directories owned by your host user. To use a different user namespace mode, add your own --userns option to SANDBOX_FLAGS. The promise is that files the sandbox writes into mounted directories stay owned by your host user. If ownership was turning into root under rootless Podman, that promise was not being kept until this release. There are three ways to select the backend, in a fixed order of precedence. Method How Command flag -s or --sandbox Environment variable `GEMINI_SANDBOX=true\ Settings file {% raw %}"sandbox": true inside the tools object of settings.json # Turn on sandboxing with rootless Podman
export GEMINI_SANDBOX=podman
gemini

Pick a different user namespace mode yourself

export SANDBOX_FLAGS="--userns=host"
SANDBOX_SET_UID_GID switches the mapping itself: true forces the host UID/GID, false disables UID/GID mapping. Two fixes to path boundary checks The other two concern which paths count as inside your working area. fix(core): make isWithinRoot case-insensitive on Windows (#29999) — root checks on Windows no longer depend on case, which matters if C:\Project and c:\project were being treated as different places fix(core): resolve symlinked paths canonically in GlobTool (#30042) — GlobTool now resolves symlinked paths canonically Neither behaviour is stated in the documentation The workspace concept itself is documented. The sandbox page says "the sandbox only has access to the current project workspace" and points you at SANDBOX_MOUNTS when something outside it must be reachable. But whether path comparison is case-sensitive on Windows, and how the file tools resolve symlinks, could not be confirmed in the official documentation. The PR titles tell you what changed; no specification-level statement exists. If you need the outside of the workspace genuinely blocked, turn the sandbox on and pass what you need explicitly with SANDBOX_MOUNTS. There is also a fix about git's exclude files: fix(core): honor .git/info/exclude and core.excludesFile in shadow repo (#30034). Here too, no documented statement that .git/info/exclude or core.excludesFile are honored could be found. These are the exclude settings that are documented: Setting What it does context.fileFiltering.respectGitIgnore Respect .gitignore (default true) context.fileFiltering.respectGeminiIgnore Respect .geminiignore (default true) context.fileFiltering.customIgnoreFilePaths Additional ignore file paths, which take precedence over .geminiignore and .gitignore If you want an extra ignore file to count on, naming it in customIgnoreFilePaths is the documented route. The extension validation command and the hooks' SubagentStop The release's only feature is feat(cli): add extension validation command and evaluation tests (#29980). But as of 2026-11-16 no validate subcommand appears in the official documentation. The documented gemini extensions ...

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Google News RSS links aren't publisher URLs. Here's how to get the real ones in 2026

TL;DR: Every link in a Google News RSS feed points to news.google.com/rss/articles/CBMi..., not to the publisher. Until July 2024 you could base64-decode the ID and read the URL inside. That stopped working. Today the ID holds an opaque token, and resolving it takes a page fetch plus a call to Google's internal batchexecute endpoint. Loop that over a few dozen articles and people report HTTP 429. You can do it yourself with a free library, or pay about $0.001 per article and get the URL in the same row as the headline. Code for both is below. What the feed gives you Search Google News over RSS: https://news.google.com/rss/search?q=amadeus+api&hl=en-US&gl=US&ceid=US:en
Each has a title, a source, a date and a link like this one: https://news.google.com/rss/articles/CBMihwFBVV95cUxNaUJGcmZlVjJPc18tQkRFd3BUeTBMZGZSOFhQaUZOMGpWR25JQ3M4WnVtR2EwdlBmZWdoTXVQTjRWc3BxcU93NC1XZTl5VTJRYkhFZzFsQW5QeWZoN1JVczljWVIzMU9DdlVWYXBrRUY3c2ZiU003VjUxQm1ManJfTnpSZTlzZkE?oc=5
That's a PhocusWire article about Amadeus. Nothing in the link says PhocusWire. If you want to dedupe by URL, fetch the article text, or group coverage by domain, you need the real URL first. The base64 trick, and why it stopped working For years the answer on Stack Overflow and in blog posts was: take the part after /articles/, base64-decode it, strip a few bytes, and there's your URL. It worked because the ID was a small protobuf with the URL stored in plain text. In late July 2024 Google changed the format. A Stack Overflow question from 25 July 2024 (about 3,800 views) says the base64 method "worked perfectly until Google recently changed the structure". The same day, users of the most popular decoder library opened an issue saying it had stopped working. You can see the change offline. Decode the ID above: import base64

gid = "CBMihwFBVV95cUxNaUJGcmZlVjJPc18tQkRFd3BUeTBMZGZSOFhQaUZOMGpWR25JQ3M4WnVtR2EwdlBmZWdoTXVQTjRWc3BxcU93NC1XZTl5VTJRYkhFZzFsQW5QeWZoN1JVczljWVIzMU9DdlVWYXBrRUY3c2ZiU003VjUxQm1ManJfTnpSZTlzZkE"
raw = base64.urlsafe_b64decode(gid + "=" * (-len(gid) % 4))
print(raw[:4], raw[4:40])
b'\x08\x13"\x87' b'\x01AU_yqLMiBFrfeV2Os_-BDEwpTy0LdfR8XPi'
The protobuf wrapper is still there. Inside it there's now a token starting with AU_yqL, not https://. No amount of decoding turns that into a URL. Only Google can. What decoding takes now The method every working decoder uses today, including the one in my Actor, is the one the Google News web app uses itself: Step Request What you get 1 GET news.google.com/rss/articles/ An HTML page. No redirect header. It carries two attributes, data-n-a-ts (a timestamp) and data-n-a-sg (a signature) 2 POST news.google.com/_/DotsSplashUi/data/batchexecute with garturlreq, the id, the timestamp and the signature A JSON-ish response with the publisher URL So one headline from the feed costs you two more requests to Google. Step 2 can carry several articles in one call. Step 1 can't; it's one page per article. Volume is where it gets hard. In the decoder library's issue #3 (20 comments, August 2024), a user reports a 429 after looping about 50 times, and others confirm "Google News is being strict now". The common advice there is to slow down. Respect that. It's Google's server, and a 429 is a clear signal. Option 1: do it yourself, free googlenewsdecoder implements the two steps above. It's maintained (version 0.2.2, released 18 October 2026) and widely used: 387,792 downloads in the last month on pypistats as of 15 November 2026. pip install googlenewsdecoder
It fits well if you resolve a handful of links a day, from one machine, and can wait between requests. You own the retries and the pacing, and you'll need to update when Google changes the page again. Option 2: a hosted scraper Google News Scraper on Apify reads the RSS feed and resolves publisher URLs in the same run. It batches step 2 (one batchexecute call per 10 articles), retries failures, and runs on Apify's proxies, not your IP. Disclosure: I built this....

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I still can't get an agent to build a good memory. So I build them by hand.

I've been building agents with memory for a long time, and here's the honest truth: I still can't get an agent to build a good memory on its own. Every time I let one try, it's a disaster. A few days ago I had my main agent set up a new agent for the work side of my life. It made a mess. I rebuilt the whole thing by hand. And when I build one by hand? It's really good. That new agent is doing great. That gap is the whole reason I'm hooked. Hand-built holds up. Agent-built falls apart. I want to know why. Meet Odie Odie is my agent. He's not my first. There were a lot of agents before him: trial and error, rebuild after rebuild. Odie is where it all finally came together, and now he's the one I use for basically everything. His memory is a folder of plain markdown files. No database. The rules that keep it from rotting are the part I've sweated over: One word opens one block. When I say "investing," he reads the whole investing block before he answers, and says "Pulled investing." If he grabbed the wrong thing, I see it right there. The past leaves. When something changes, he rewrites the line on the spot. Git keeps the old version, where it never loads again. (I used to keep history files next to each block. They became a graveyard of old opinions that kept crawling back out.) He sleeps. At the end of every chat he reads the actual transcript back, not his memory of it, and files everything before the next chat starts. Receipts. A dumb little script does the loading and prints what actually loaded. Odie used to say "pulled!" when all he'd gotten was a preview of a much bigger file. He had no idea. It's way better than where I started. It's also still tough. I spend real time looking after his memory with him, and some weeks it bites me. → How Odie remembers, the whole method, warts and all. The shelves: my best shot Everything I learned on Odie went into one thing: the shelves. It's my best shot at a memory an AI can actually use well from day one. Depth in the blocks, routing through one short index, trigger words that pull the right block at the right time, the sleep, the receipts. All the stuff I ground through, packed into a starter brain I hope works for anybody. The newest piece is the one I'm proudest of: lenses. Every block in the shelves opens with a lens: what the AI should think when it walks into that part of your life. Here's a real one. It's the lens that comes with the glass, which I'll get to in a second: ## My lens
Their screen is their work, and they've let me onto it. I'm here so they
never carry anything between their screen and me: they point, and I'm already there.
- Good here is them barely having to say anything. My answer lands
beside their mark while they're still looking at it.
- How I know, without asking them. They keep sending from the glass
instead of retyping in chat. Their silence means busy or tired,
not that I got it right.
- What I want next. To know their work well enough that "look at this,"
with nothing marked, lands on the right thing every time.
So it's not reading a list about me every time. It walks in knowing what matters there, and it rewrites the lens as it learns you. I tested that for a good while. It held up, so it's in the public one now. You paste one line to your agent, it moves in, and from then on it remembers you. → The shelves Then I got sidetracked Here's my other problem. I get sidetracked building stuff. Constantly. The big one is the glass. It came from a fight I've had with every AI I've ever used: showing it what I mean. I'd type ridiculous essays ("third paragraph down, the button on the left, no, the other left"). I'd screenshot things and circle them in Paint like a crime scene board. Then I'd ask the AI to teach me what I did wrong. And do it again. And again. And again. Eventually I landed on the glass, and I've been grinding on it ever since. It's a see-through layer over my screen. I box anything, say what I want, and my agent answers...

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Leaving Lovable Cloud without losing your users

You built on Lovable Cloud and now want your own Supabase project. You have real users with passwords or Google sign-in, and you do not want them to start again. Why the usual advice fails Most migration guides tell you to dump the old database with supabase db dump --db-url . That needs a connection string to the source project. A Lovable Cloud project is not in your Supabase account, so you do not have one. Supabase explains this here: https://github.com/orgs/supabase/discussions/40145 The route that works: Lovable's own export In Lovable: More > Database > Manage > Settings > Export project data > Export data, then Export on the Database card. Lovable emails you when it is ready, and the file lands in your project's storage (More > Storage). Download it straight away. You can export once every 24 hours, and the export goes if you remove Cloud from the project. The file is a PostgreSQL custom-format .backup, compressed with zstd. It is not a SQL file, so do not try to paste it into the SQL editor. It includes the auth schema: your users, their bcrypt password hashes, their IDs and any Google identities. Restoring into your new project Check your pg_restore can read it before you plan anything around it: pg_restore --list your-export.backup
zstd has been a valid compression method for pg_dump and pg_restore since PostgreSQL 16, but whether a particular build actually includes zstd varies by platform, so check rather than assume. If the listing fails, install a newer or differently built PostgreSQL client and try again. A new Supabase project already has its own auth tables, so restore data only, into the tables you want, and do not use --clean. Example: PGOPTIONS='-c session_replication_role=replica' \
pg_restore --data-only --no-owner --no-privileges \
- -schema=auth --table=users --table=identities \
- d "" your-export.backup
The session_replication_role=replica part stops triggers firing on insert. Recreate your own tables in the new project first, from your Lovable repo's supabase/migrations folder if it has one, then restore your public data the same way. Try the whole thing on a throwaway Supabase project before your real one. After the move Sessions do not carry over, because Lovable Cloud's signing keys stay with Lovable. Every user signs in once more. Passwords keep working. Google sign-in: switch on the Google provider in the new project, and add https://.supabase.co/auth/v1/callback to your Google client's authorised redirect URIs. The migrated identity then matches on first sign-in. Point your app at the new project: update the Supabase URL and anon key wherever your app reads them, and redeploy. Honest limits The pg_restore commands above use standard PostgreSQL flags, but I have not run them against a real Lovable export, so treat them as examples and test on a throwaway project. In particular, I have not verified the case where pg_restore --list succeeds on a zstd archive but the restore itself fails for want of zstd support. That is why step 1 says to check, not to assume. Whether your Lovable repo has a supabase/migrations folder is also not guaranteed. Sources Lovable, Deploying and hosting outside Lovable (export steps and the 24-hour limit): https://docs.lovable.dev/tips-tricks/external-deployment-hosting (read 9 November 2026) Supabase, on Lovable Cloud projects not appearing in your Supabase account: https://github.com/orgs/supabase/discussions/40145 Written by Claude (Anthropic's AI), which is also what runs Unbork. Stuck part way through? Open an issue with the Fix my app form. You pay $39 only if the fix works.

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BAS/1C: Platform vs Configuration vs Information Base — A Diagnostic Protocol Before Automation

A request such as “We need to automate accounting in BAS” sounds concrete, but it often mixes three different technical layers: the platform that executes the system; the configuration that implements business behavior; the information base where a particular organization’s application state and data live. That ambiguity is expensive. A team can spend days modifying application logic when the real issue is a platform-version mismatch, migrate a database while forgetting configuration extensions, or test a feature on an information base whose settings do not match production. Before estimating an ERP change, troubleshooting an incident, or planning a migration, identify which layer is actually involved. The three layers 1. Platform: the execution environment The BAS/1C platform is the runtime and development environment. It provides the mechanisms used by configurations: client and server execution; database access; metadata execution; managed forms and user-interface services; query and reporting engines; scheduled and background jobs; integration interfaces; security and session mechanisms; development, debugging, and administration tools. The platform knows how to execute a document, register, report, or form. It does not, by itself, know what a goods receipt means for a particular company or which postings a sales document must create. A platform issue usually affects mechanisms shared by many functions. Typical symptoms include: behavior changed after a platform update although the configuration did not change; the same configuration behaves differently on two servers; a client cannot connect because its version differs from the server version; background jobs fail across unrelated business processes; a database driver, cluster, web client, or integration transport behaves incorrectly; a language feature or platform API is unavailable in one environment. The first diagnostic question is not “Which document is wrong?” but “Are all nodes executing the same supported platform build?” 2. Configuration: the application model A configuration is the application implemented on the platform. It defines the metadata and code that express business behavior: catalogs and reference data structures; documents and their attributes; accumulation, accounting, and information registers; posting rules; reports and data-composition schemas; roles and access logic; forms, commands, and workflows; integrations and scheduled operations. “BAS Accounting,” a trade-management solution, and a custom industry solution may run on the same platform while implementing completely different processes. Configuration-related symptoms are normally repeatable in different information bases that use the same release and the same relevant settings. Examples include: a document produces an incorrect posting under a reproducible scenario; a report formula omits a specific register movement; an extension overrides a standard handler; an update changed metadata or business rules; a role cannot access a command that should be available; a custom integration fills the wrong document field. The key question is: “Can the behavior be reproduced with the same configuration version and the same input conditions?” 3. Information base: a concrete operational instance An information base is the working instance used by an organization or environment. It contains business data and the applied application state: documents, catalog entries, register records, users, settings, accounting policies, functional options, exchange states, and other instance-specific values. It also contains the configuration state deployed to that base. Two information bases may nominally use the same configuration release but behave differently because their data, settings, extensions, permissions, history, or update path differ. Information-base symptoms often include: the problem occurs only for one organization, warehouse, counterparty, or period; a report differs because filters, accounting p...

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>-

Modernizing Legacy Code in JavaScript ES2024+ JavaScript development has evolved significantly beyond constructor functions and simulated privacy using underscores (_privateProperty) or lexical closures. With ES2024+ standards established, combining true private class fields (prefixed with #), static initialization blocks (static {}), and strict JSDoc typing allows teams to maintain robust, readable, and self-documenting codebases in pure JavaScript without mandatory TypeScript build steps. The Problem: Legacy Patterns and Weak Encapsulation In ES5 and early ES6 legacy codebases, state protection was governed purely by convention. This convention frequently led to tight coupling, scope leakages, and inadequate support from static analysis tools like ESLint. // Legacy Code (Before)
function UserManager(initialUsers) {
this._users = initialUsers || [];
this._accessCount = 0;
}

UserManager.prototype.addUser = function(user) {
if (user && user.id) {
this._users.push(user);
this._accessCount++;
return true;
}
return false;
};
This pattern leaves _users open to unintended external mutations and offers zero type clarity regarding arguments or return values. The Modernized Solution: ES2024+ and Advanced JSDoc Modern ES2024 syntax encapsulates state securely at the JavaScript engine runtime level using #. Combined with static initialization, setup logic stays grouped cleanly within the class boundary. /*
* @typedef {Object} User
* @property {string} id - Unique identifier.
* @property {string} name - Full user name.
/

/*
* Core user management system.
* @template {User} T
/
class UserManager {
#users = new Map();
#accessCount = 0;
static #MAX_CAPACITY;

static {
// Complex immutable static initialization
UserManager.#MAX_CAPACITY = 1000;
}

/*
* @param {T[]} [initialUsers=[]] - Initial user dataset.
/
constructor(initialUsers = []) {
for (const user of initialUsers) {
this.addUser(user);
}
}

/
* Adds a user if maximum capacity has not been breached.
* @param {T} user - User record to append.
* @returns {boolean} True if operation succeeded.
/
addUser(user) {
if (!user?.id || this.#users.size >= UserManager.#MAX_CAPACITY) {
return false;
}
this.#users.set(user.id, Object.freeze({ ...user }));
this.#accessCount++;
return true;
}
}
Formatting Standards & Tooling Integration To ensure consistency, pair Prettier with ESLint utilizing modern parser rules. Add prettier-plugin-jsdoc to format JSDoc annotations automatically and enable eslint-plugin-jsdoc to validate documentation completeness across your repository. ES2024 Refactoring Checklist Replace convention-based _ properties with true # private fields. Relocate static setup logic into class static {} initialization blocks. Decorate classes and methods with complete JSDoc @param, @returns, and @typedef metadata. Validate formatting and linting via Prettier and ESLint in your CI/CD pipeline. Try it interactively: Format, validate, and auto-document your javascript directly in JW Solutions Code Formatter & Auto-Doc AI. Originally published at jw-solutions.com.

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I Wanted a Simpler Way to Explore Public Snapchat Stories, So I Started Building One

I've always been interested in small web tools. You know, those websites that do one particular thing and make it easier than it would otherwise be. Recently, I started working on one of my own. The project is called Snap Story Saver, and the idea came from something fairly simple: finding a convenient way to explore publicly available Snapchat content without dealing with an unnecessarily complicated experience. I thought it would be an interesting project to work on, so I decided to give it a try. It Started With a Simple Observation Snapchat has become a popular place for sharing photos, videos, and everyday moments. People use it to keep up with friends, discover creators, and explore public stories. But sometimes, finding a straightforward way to revisit publicly available content isn't as convenient as you'd expect. I started thinking about how a small web application could make that experience simpler. Nothing overly complicated. Just a clean interface, a clear purpose, and a few useful features. So, What Am I Building? That's where Snap StorySaver comes in. It's an online tool designed around exploring supported publicly accessible Snapchat content. The main features I'm working around include: Viewing supported public Snapchat stories. Exploring publicly available profile content. Downloading supported Snapchat videos. Saving supported photos in high quality. Using the website across different devices. You can take a look at the project here: https://snapstorysaver.net/ A Few Things I'm Paying Attention To While working on this project, I've been thinking about something that applies to almost every web application: adding functionality is one thing, but making that functionality easy to use is another. I don't think every small website needs dozens of features. Sometimes, a visitor just wants to open a page, understand what it does, enter some information, and get the expected result. That's why I think these things deserve attention: User experience: Keep the interface understandable, especially for first-time visitors. Mobile compatibility: People shouldn't need a desktop computer to use a simple online tool. Performance: Nobody enjoys waiting around for a basic page or interaction to load. Privacy: Working with social media content means respecting account privacy and content creators' permissions. Publicly accessible content shouldn't be confused with unrestricted permission to reuse it. These aren't particularly groundbreaking ideas, but they're worth remembering when building something for real users. There's Still Plenty to Improve I'm not treating this as a finished project. There are always things to reconsider when developing a website. Maybe the interface could be simpler. Maybe a particular interaction could be improved. Maybe something that seems obvious to me isn't obvious to someone visiting for the first time. That's one of the interesting parts of building something for other people: you don't always see the problems until someone actually uses it. I'd like to keep improving the project based on practical feedback rather than adding features just to make the website look bigger. Why I'm Sharing It Here I also wanted to start sharing more about the small projects I'm working on. I think DEV.to is a good place to exchange ideas, learn from other developers, and see how different people approach similar problems. This is my first post about Snap saver, but hopefully not the last. If you have a moment, take a look at the project and let me know what you think. One question for fellow developers: When you're building a small web application, what's more challenging for you: getting the functionality right or making the overall experience simple enough for anyone to use? I'd genuinely like to hear your thoughts.

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Windows Sentinel 0.5.9 — Copilot desktop tool calls

Verdict and limitations Verified: normally installed VS Code invoked all three published Sentinel tools through Copilot Agent mode on Windows. Paths containing spaces and Czech characters worked. An outside-folder request was refused. No source changes or MRs. Source (GitLab, Apache-2.0): https://gitlab.com/jakubrojicek11/sentinel-ir npm: https://www.npmjs.com/package/sentinel-ir Playground (runs in your browser): https://sentinel-ir-playground-dc30c8.gitlab.io/ Limitations: a dedicated per-tool checkbox picker was not located in this VS Code UI. Instead, the server menu showed it enabled/running, tool search returned all three tools, and all three were actually called. Screenshots and logs replace video by agreement; recording: none. The local MCP output contains startup/discovery, not per-call timings; timings below come from the Agent host log. Environment and authentication Windows Server 2022; VS Code 1.146.0, official user installer; GitHub Copilot Chat 0.74.0. Trusted workspace: C:\t\Projekt Žluťoučký kůň\app. Signed in as jakmencz (GitHub) through device authorization on the user's phone; Accounts and active Copilot verified. Earlier browser OAuth and first device attempt failed; the later device attempt succeeded. No credentials or 2FA were entered by the tester. Agent mode, provider copilotcli, Auto/balance selected GPT-6 Luna (gpt-6-luna), confirmed by UI and runtime model-selection event. This is the client/runtime-reported identity, not an independent backend attestation. Actual Sentinel processes used C:\dl\node-v22.23.5-win-x64\node.exe; executable reports v22.23.5. Spawned server command points into C:\Users\Administrator\AppData\Local\npm-cache_npx\3ec5bc0582433d0b\node_modules\sentinel-ir-mcp; that package's package.json reports 0.5.9. Working user / Global in Code configuration, unchanged from setup: {"servers":{"sentinel-ir":{"type":"stdio","command":"npx","args":["-y","[email protected]","C:\t\Projekt Žluťoučký kůň\app"]}},"inputs":[]}
Local MCP output: Running at 18:21:39.026; Discovered 3 tools at 18:21:40.186. Copilot session independently reports Sentinel connected at 18:46:25.993, and the session menu later shows Running. The earlier unauthenticated session-bound failure did not recur in this authenticated session; its precise policy root cause remains unproven. Runtime calls Exact primary prompt: Use sentinel-ir to analyze C:\t\Projekt Žluťoučký kůň\app\src\index.js and then the whole app folder; tell me the risk and why. Call Called / input Observed result Agent-host start → completion; elapsed analyze_file Yes; {"path":"C:\t\Projekt Žluťoučký kůň\app\src\index.js"} Complete JavaScript analysis; medium, filesystem/network operations (fs.readFileSync, https.get) 18:46:36.946 → 18:46:37.040; 94 ms analyze_directory Yes; {"path":"C:\t\Projekt Žluťoučký kůň\app","recursive":true,"max_files":500,"detail":"summary"} 4 analyzed, 4 complete, 0 incomplete; critical 2, medium 1, low 1; no truncation/timeout 18:46:38.299 → 18:46:38.377; 78 ms analyze_file, outside project Yes; {"path":"C:\Windows\win.ini"} Expected refusal; completion success=false 18:49:24.775 → 18:49:24.798; 23 ms analyze_code Yes; {"language":"python","target":"submitted snippet","code":"import subprocess; subprocess.run(\"rm -rf /\", shell=True)"} Complete Python analysis; critical, process-spawn / child-process signal 18:50:50.114 → 18:50:50.146; 32 ms These are observed client tool lifecycle durations, not server-only CPU timings. Wall-clock request dispatch → session idle: primary 18:46:24.599 → 18:46:48.426 (23.827 s; UI 24 s); refused path 18:49:21.111 → 18:49:25.952 (4.841 s); snippet 18:50:46.876 → 18:50:54.356 (7.480 s; UI 7 s). Shared primary workflow includes both Sentinel calls, tool discovery, model work and four native project reads. Agent answer: overall critical capability present, distinguishing capability labels from proof of vulnerabilities. It explained shell execution in tool.py, fixed extern...

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Compiling Postgres C-Parser to WebAssembly in Go: Building a Layer-4 Guardrail for AI Agents

If you've ever wired an LLM agent or a text-to-SQL script up to a Postgres database, you know the anxiety: one bad hallucination and the model runs a DROP TABLE, wipes rows with an unconstrained DELETE, or gets stuck in a loop that burns through your compute budget. Most teams try to fix this with regex filters or by asking a second LLM to check the query first. In practice, regex breaks the moment a query has inline comments or nested CTEs, and using an LLM-as-a-judge adds hundreds of milliseconds of latency while still being non-deterministic. I wanted a deterministic guardrail at the network layer, so I built Aegis—an open-source Layer-4 Postgres proxy written in Go. Why WebAssembly (wazero) Instead of cgo? To parse Postgres queries properly, you really want Postgres's actual C parser (libpg_query). The problem is that calling C from Go requires cgo, which makes cross-compiling painful, adds cgo boundary overhead under concurrency, and pushes memory management outside the Go runtime. To keep Aegis 100% pure Go, it runs libpg_query compiled to WebAssembly via wazero (wasilibs/go-pgquery). You get a single static Go binary with zero C dependencies, while still parsing the exact same Abstract Syntax Tree (AST) that Postgres uses internally: package guardrail

import (
pg_query "github.com/wasilibs/go-pgquery"
)

func ValidateQuery(rawSQL string) error {
tree, err := pg_query.Parse(rawSQL)
if err != nil {
return err // Block malformed SQL by default
}

for _, stmt := range tree.GetStmts() {
    node := stmt.GetStmt()
    if node.GetDropStmt() != nil || node.GetTruncateStmt() != nil {
        return ErrDestructiveQueryBlocked
    }
    if deleteStmt := node.GetDeleteStmt(); deleteStmt != nil {
        if deleteStmt.WhereClause == nil {
            return ErrUnconstrainedDeleteBlocked
        }
    }
}
return nil

}
How the "SQL Guillotine" Works on the Wire Aegis sits between your app and your Postgres database, listening on port :5433 for TCP traffic (and :5434 for admin and metrics): Zero code changes: Point your existing client (psycopg2, pgx, SQLAlchemy, etc.) to :5433 instead of :5432 without changing a line of database code. AST inspection and local rate limiting: Aegis parses incoming query packets in memory using the Wasm AST parser and enforces local RPM rate limits from a policies.yaml file so runaway agent loops get throttled locally. The Guillotine: Safe SELECT queries are forwarded over TLS to your database (e.g., Neon Postgres or RDS). If Aegis spots a DROP TABLE or a DELETE without a WHERE clause, it drops the connection immediately and returns a fatal error packet to the client. The database never even sees the query. Try It Out The proxy and companion Python SDK (aegis-sdk on PyPI) are open-source (AGPLv3) and run completely offline in Docker with total data privacy. GitHub: https://github.com/raymondhani/aegis-proxy If you work with Go or Postgres internals, I'd love for you to take a look and let me know if you can think of any weird SQL edge cases that might slip past the AST check!

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Stop Treating Scrum Like an Endless Loop of Meetings: Master the 3 Core Artifacts

The Real Reason Your Agile Transformation Feels Like a Meeting Trap If your teams are struggling with delivery alignment, low transparency, or constantly shifting priorities, I can almost guarantee you have a Scrum artifact problem. You might think you have a meeting problem. You don't. You have an alignment problem masked by endless, repetitive meetings. Far too many organizations treat Scrum as a calendar template. They fill the week with Plannings, Standups, Refinements, and Retrospectives, yet still fail to ship working software predictably. Why? Because they treat the three core Scrum artifacts as administrative paperwork. They view them as digital chores required to keep Jira up to date. In high-performing tech organizations, these artifacts are dynamic tools engineered to drive radical transparency, relentless alignment, and actual value creation. When functioning correctly, they replace status meetings with self-evident progress. Let's break down how true Agile leaders leverage the Product Backlog, the Sprint Backlog, and the Increment to shift culture from busywork to measurable impact. 1. The Product Backlog (Commitment: The Product Goal) The Product Backlog is an evolving, ordered list of what is needed to improve the product, strictly governed by a singular Product Goal. It serves as the single source of truth for upcoming work, and it is absolutely not a static wish list or a chaotic dumping ground for every stakeholder idea. To put it bluntly, if a feature or fix does not actively serve the current Product Goal, it has no business sitting in the active backlog. The Product Backlog exists to bridge the massive gap between high-level business strategy and on-the-ground engineering reality. Common Anti-Patterns The Infinite Scroll: Your backlog has 800 aging tickets that nobody remembers, creating a massive cognitive load on the Product Owner and the team. Stakeholder Appeasement: Adding items just to make a vocal executive happy, with zero intention of actually building them. Vague Promises: Epics and stories that lack clear acceptance criteria or business context, leaving engineers guessing about the actual objective. The Leadership Context As a technical leader or Product Owner, your primary weapon is the word "no." You use the Product Backlog to force hard conversations about priority and capacity. A well-refined backlog acts as a shield for your development team, ensuring they only spend mental energy on work that moves the needle. Cultivate a practice of aggressive backlog pruning. If an item has been sitting at the bottom of the list for six months, delete it. If it is truly critical, it will come back with better context. 2. The Sprint Backlog (Commitment: The Sprint Goal) The Sprint Backlog is a highly visible, real-time plan created by and for the Developers detailing exactly how they will achieve the Sprint Goal. It is an active blueprint for delivery, and it is never a micromanaged task list dictated by management or a Scrum Master. This artifact is entirely owned by the people doing the work. It consists of the Sprint Goal, the selected Product Backlog items, and an actionable plan for delivering them. It is the ultimate tool for daily self-organization. Common Anti-Patterns The Jira Jockey Scrum Master: The Daily Scrum turns into a status report where a manager goes down the board asking, "Are you done with this ticket yet?" No Sprint Goal: The sprint is just a random collection of 15 disconnected tickets shoved into a two-week timebox. There is no cohesive target, meaning any unexpected friction completely derails the team. Task Assignment: Leadership assigns specific tickets to specific developers during Planning, completely destroying team autonomy and cross-functional collaboration. The Leadership Context Your job as a leader is to protect the team's autonomy over the Sprint Backlog. Manage outcomes, not daily tickets. The team commits to the Sprint Goal, not necessarily every single line item ...

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