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I built a debugging-practice site where student code runs entirely in the browser . Python via Pyodide , JavaScript in a worker. No server executes anything. No execution bill, no queue, no sandbox to maintain. But four things bit me hard. 1. Your arguments aren't Python objects Pass a JS object into Python and you get this: TypeError: 'pyodide.ffi.JsProxy' object is not subscriptable It's not a dict . It's a live view of the JS object, and it supports neither obj[key] nor .get() . Convert explicitly: const pyArgs = input . map (( arg ) => pyodide . toPy ( arg )); const result = fn (... pyArgs ); 2. null is not None This one passed my entire test suite while being broken in production. pyodide . toPy ( null ) check result type(v) JsNull bool(v) False ✅ falsy, as expected v is None False ❌ the surprise It's falsy, so truthiness checks work fine. But is None fails — which was exactly what my code was checking. Why my tests missed it: the harness used json.loads . The app used toPy . Different conversion paths, different answers. If you need a real None , create it in Python. Don't pass one across. 3. sys.settrace is a free step debugger Want to show users their code running line by line? Python basically hands it to you: def _tracer ( frame , event , arg ): if frame . f_code . co_name != target : return None # skip library frames if event == " line " : steps . append ({ " line " : frame . f_lineno , " locals " : dict ( frame . f_locals ), }) return _tracer Two things this naive version gets wrong: Add a step cap. A tight loop generates steps faster than it burns a 5-second timeout. You need both guards. Handle exception . During unwinding, the return event still fires with arg=None . Miss it and your trace says "returned None" for code that crashed. 4. Your snapshots are lying A user screenshot exposed this one. Every step in the trace showed the final state of a list. Step 1 included mutations that hadn't happened yet. tracing: nums = []; nums.append(1); nums.append(

Hay muchísimo escrito sobre qué tiene que reportar una organización: guías de Supersalud, de Supersociedades, de SAGRILAFT, de PTEE, de SST, de reportes ambientales. Todas contestan la misma pregunta — ¿qué me aplica? — y la contestan bien. Casi nadie escribe sobre la pregunta que de verdad hace fallar a las organizaciones: ¿cómo no perder ninguno, todos los años, cuando son treinta? Porque los incumplimientos que he visto de cerca casi nunca vienen de que alguien ignorara la obligación. Vienen de que la obligación se conocía perfectamente y aun así se pasó la fecha. Este artículo va del problema operativo —fechas, evidencia, responsables— no de cuáles normas le aplican a su entidad. Eso es otra conversación, y no es esta. Por qué el archivo de Excel deja de servir Con tres obligaciones, una hoja de cálculo sobra. El dolor no empieza por el número: empieza cuando el calendario hay que derivarlo . 1. Las fechas no son fechas, son reglas. Muchos vencimientos no están escritos como un día del calendario: dependen del último dígito del NIT, de días hábiles, o de un plazo contado desde un hecho. Eso significa que alguien recalcula el calendario entero cada año, a mano . Cada enero se reintroduce la misma oportunidad de equivocarse, y basta con un festivo mal contado. 2. El calendario vive en una persona. Casi siempre hay alguien que "sabe cómo es la cosa". Mientras esté, funciona. Cuando se va de vacaciones —o se va de la empresa— se va con ella el contexto que nunca estuvo escrito. La hoja sobrevive; el criterio para llenarla, no. 3. La hoja dice que se entregó, no lo prueba. La celda en verde es una afirmación de alguien. La evidencia real —el radicado, el archivo exacto que se subió, la hora— está en el correo de alguien. El día que hay que demostrarlo, empieza la arqueología en bandejas de entrada. 4. Los terceros que le reportan a usted. Si recibe información de contratistas, sedes o filiales, ahora administra dos problemas: sus propios vencimientos y los de ellos.

I built an orchestration skill for Claude Code that delegated everything to subagents. It worked. It also cost somewhere on the order of 1-2 million Opus tokens per task - including tasks whose final diff was a handful of lines. Nothing was broken. Every individual decision was defensible. Three modest multipliers stacked, and then the whole stack ran on every single request. This is the postmortem, the redesign, and the enforcement layer I should have written first. v1: pure delegation The design goal was context hygiene. The main session gets polluted fast - it accumulates file contents, tool output, and dead ends, and its judgment degrades as the window fills. So: don't let it do any work. Make it a coordinator, and give every unit of real work a fresh context. That produced four rules: A hard gate. The main session was forbidden from reading, editing, or running anything itself. Every action went through a subagent. A fixed 5-phase pipeline on every task: Plan → Approve → Execute → Review → Report. Fresh subagents per phase. No reuse. Each phase got clean context by construction. Mandated reviewers with "loop until clean." A review phase that re-ran until it found nothing. And the trigger was broad - essentially any actionable request. "do this," "implement," "fix," "build," "change." Read those four rules again with a cost lens instead of a correctness lens. That is the whole postmortem. The three multipliers 1. The dispatch schema made model optional The subagent dispatch tool takes a model parameter. My skill never set it. Omitted, it inherits from the parent session - which was Opus 4.8. So every subagent, including the ones whose entire job was "read this file and summarize it," ran on the most expensive tier available. Here's what that actually costs at list prices: Model Input $/MTok Output $/MTok vs. Opus Claude Opus 4.8 ( claude-opus-4-8 ) $5.00 $25.00 1× Claude Sonnet 4.6 ( claude-sonnet-4-6 ) $3.00 $15.00 0.6× Claude Haiku 4.5 ( claude-haiku-4-5 ) $1.

submitted by /u/f311a [link] [留言]

"Because typing the same 15 commands every day is so 2026." A command-line utility that turns your chaotic terminal sessions into a beautiful, interactive menu. Stop memorizing commands. Start executing like a pro. 🚀 What Makes This Tool Special? Feature What It Does For You 🎯 Zero Memorization Never type kubectl get pods --all-namespaces --context=prod again ⚡ Lightning Fast One binary. No dependencies. Runs everywhere. 🔗 Command Chaining Execute complex workflows with --exec "1-2-3-4" 📁 Team-Ready Share menu.yml with your team. Onboard new devs in 30 seconds. 🔐 Env Variables Store secrets safely in env.ini — never hardcode credentials 📦 Installation (30 seconds or less) Option 1: One-Liner (if binary is hosted) curl -sSL https://example.com/hello | sudo tee /usr/local/bin/hello && sudo chmod +x /usr/local/bin/hello Option 2: Build from source git clone https://github.com/yourrepo/hello cd hello go build -o hello main.go ./hello --help Option 3: Copy & Go # Anywhere you want: cp hello ~/hello # Home folder cp hello /usr/local/bin/ # Global access (recommended) 🎮 Usage That Will Make You Smile Interactive Mode — The "I'm Feeling Lazy" Way # Just run it. The menu will greet you. ./hello # Using your own config ./hello -c ./deploy_menu.yml Headless Mode — The "I'm Automating Everything" Way # Execute a single command ./hello --exec "1" # Execute a whole pipeline (1 → 2 → 3 → 4) ./hello --exec "1-2-3-4" Perfect for: CI/CD pipelines, morning standup scripts, and impressing your boss. 📂 Example Menu (Your New Best Friend) items : 1 : title : " 1. 🚀 Deploy to Production" commands : - " git checkout main" - " git pull origin main" - " docker build -t myapp:latest ." - " docker push myapp:latest" - " kubectl rollout restart deployment/myapp" 2 : title : " 2. 📊 Check System Health" commands : - " htop" - " df -h" - " free -m" - " netstat -tulpn | grep LISTEN" 3 : title : " 3. 🔥 Clean Up Docker Garbage" commands : - " docker system prune -af --volumes" - " echo '✨ Saved 47 GB

As the title suggests, because there's no data on Papercopilot yet, and people have been talking about the scores being lower in general than last year, I thought it could be interesting to survey the average score distribution after the rebuttal phase (not considering confidence weights). Very rough and simple poll (I also realize there's a self-selection bias in there). Cast your vote here: https://loppy.be/poll/yczuv8yo Thanks! Edit: the trolls have taken over, never mind any notion of representativeness I guess... submitted by /u/Zhiend727 [link] [留言]

Most of the validation work on vaas-x so far had been industrial sensor data — turbofans, machine telemetry. I wanted to know if the same zero-config channel classifier actually transfers to a completely different domain: a wearable IMU strapped to a moving human. No feature engineering, no per-sport tuning, no hints about what any channel means. I'm writing this one up slightly differently than my other posts, because the first version of this test gave me a wrong answer, and I think the reason it was wrong is more useful than the result itself. The dataset UCI's Daily and Sports Activities set (Altun, Barshan & Tunçel, 2010): 8 subjects, each wearing five Xsens IMU units — torso, both arms, both legs — 9 axes per unit (accelerometer, gyroscope, magnetometer × x/y/z), sampled at 25Hz. 45 channels total. It includes both a sedentary activity (sitting) and dynamic sport activities (basketball, rowing), which gives a clean, checkable question: does a classifier that's never seen this data correctly tell apart "person sitting still" from "person playing basketball," using channel statistics alone? import pandas as pd # Mirrored subset: github.com/AniMadurkar/Daily-Activities-and-Sports-Biomechanics-Analysis df = pd . read_csv ( " sports_science_dataset_subset.csv " ) channels = [ c for c in df . columns if c not in ( " subject " , " activity " , " timestamp " )] print ( len ( channels ), " channels " ) # 45 First attempt — and the mistake My first pass pooled all 8 subjects together per activity and ran it through the profiler in one shot. The result came back backwards: sitting showed up with more "significant" channels than basketball. That's not just unexpected, it's physically nonsensical — a person sitting still should be one of the lowest-variance activities in the entire dataset. The bug wasn't in the classifier. It was in the test. Pooling subjects together means each subject's own sensor baseline and IMU orientation differences get mixed into the between-subje

The brand trip is a right of passage for influencers. It's a mark of legitimacy that a sponsor wants to invite them on an all-expenses-paid vacation, often with luxurious freebies and activities. Trips can also spur hard feelings from uninvited influencers, trigger criticism from the public, and project a certain frivolousness. Usually it is fast […]

Memory and storage prices will be inflated for the foreseeable future, so we’re always happy to find a good deal on capable gaming hardware, like this discount on the Lenovo Legion Go S with a Z2 Go processor and SteamOS. Normally $1,137.41 (and selling for close to $1,000 at Best Buy), Lenovo has the handheld […]

Waymo has dropped the waitlist for its robotaxi service in Dallas, the latest step in the company's bid to scale its self-driving technology across the United States, U.K., and Europe.

Most Windows 11 users don't need to bother with the Pro version of Microsoft's operating system.

When a premium car brand like BMW says it has a "special surprise" in store for drivers, I'd expect something more luxurious than having a movie commercial beamed onto the dashboard. That's exactly what's happening to many BMW owners, however, who are being shown banner ads for Spider-man: Brand New Day on their Control Display […]

Bingers is a new TV and movie tracker that revives the social features fans loved with TV Time, while adding support for importing their viewing history.

A security vulnerability in the cryptocurrency hardware wallet Coldcard is allowing hackers to drain the crypto from victims’ wallets. The total losses amount to more than $130 million, according to blockchain-monitoring firms.

The camera flash will evolve into a multicolor LED that illuminates when your phone is face down.

T-Mobile is launching a new financing option that will allow you to pay for a device, taxes, and fees over 36 months. In an update on Tuesday, T-Mobile says its new Equipment Installment Plan (EIP) Flex 36 requires no upfront payment and will come with a 0 percent APR for a limited time. Even if […]

Quotient CEO Lizzie Matusov explains why soaring AI spend often fails to improve software delivery. She presents a research-backed AI maturity framework designed to help engineering leaders move beyond vanity metrics like token usage, align organizational AI adoption, and address critical bottlenecks across the software development life cycle to deliver measurable business outcomes. By Lizzie Matusov

Congress set ownership limit at 39%, but FCC claims authority to kill the rule.

Spotify says Merlin, which represents more than 30,000 independent labels and distributors, has joined Universal Music Group in backing its upcoming AI-powered remix and covers product. The paid tool will let fans create AI-generated covers and remixes of participating artists’ music while ensuring artists opt in, receive credit, and are compensated.

Almost every tool-governance layer I have looked at writes its log after the call returns. Some write it in a finally . Some batch it. Some hand it to a logging framework that flushes on its own schedule. That ordering quietly decides what your log can be used for. If the record is written after the body runs, then a record that is missing has two possible explanations, and nothing in the file distinguishes them: The call was never authorised, so it never ran. The call was authorised, ran, did its work, and the process died before the log line reached disk. Those are not close together. One is the control working. The other is an unlogged deletion. When someone asks you six weeks later what your agent was permitted to do at 03:14, "there is no line for it" answers nothing. So I wrote a small library that inverts the order. obstat obstat is an auditable decision record for agent tool calls. Nihil obstat — nothing stands in the way — was the formal clearance a censor granted in writing, before publication . That is the whole idea. from obstat import guard @guard ( resource = " doc:{doc_id} " ) def delete_document ( doc_id : str ) -> str : ... An agent asks to do something, a rule decides, and the decision goes to disk — written and fsync ed — before the tool body executes. If the process dies mid-call, the record still says what was authorised, for whom, against which resource, and why. record.decision() returns only after the fsync returns. Not flushed after, not deferred, not batched. Everything else in the library is convenience; this is the part an examiner relies on. The claim has a test, not a paragraph An architectural promise nobody can falsify is marketing. This one is checked by reading the log from inside the tool body — the one place where anything buffered, deferred, or written afterwards is invisible: def test_record_is_durable_before_the_body_runs ( workspace ): workspace ( ALLOW_ALL ) seen : dict [ str , list ] = {} @guard () def read_thing ( what : st