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This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange What I Built Dead Software Library — a haunted cemetery for discontinued software, inspired by Dead Poets Society.
OpenAI quietly published openai/mcp-extensions on September 29. It is an Apache-2.0 repository with a specification, a TypeScript SDK (@openai/mcp-extensions) and a Python SDK (openai-mcp-extensions), currently at 0.1.0.
we all know the drill. your code breaks. the terminal spits out 40 lines of red text. you copy it. you paste it into google. you click a stackoverflow link from 2014. the solution doesn't work because your stack is different. you waste 45 minutes. i got tired of it. so i built KODA.
Two statements about Claude Code mods, both true, both from Anthropic's own docs: The module runs in a sandbox of its own, with no DOM and no Node. "Mods aren't sandboxed." The first two days of the mods launch have been one long argument about which of these is the lie. Neither is.
Originally published at AI Agent Security: The Runtime Surface Around Every Tool Call on smartgate.network. A shorter version of "AI Agent Security: The Runtime Surface Around Every Tool Call" — the full piece lives at smartgate.network.
Most chat apps forget you the moment the session ends. The usual fix is a backend with accounts, a vector database, and a privacy policy. If you ship a small iOS or macOS app, that is a lot to take on for "remember that I prefer short answers." I built AiPersona so the memory can live on the…
Short answer: put a chat-based classifier in front of invoice extraction and require one small JSON-schema result: allow, review, or block, plus policy categories and a reason.
Switching AI models is easy. Bringing your project context along is the annoying part. You explain what you’re building, who it’s for, and the decisions you’ve already made. Then you open another chat and explain it all again. I’m KAT, co-founder of 123sudo.
This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content What I Built A few weeks ago, I was checking my DEV.to dashboard and noticed something weird in the Traffic Sources panel.
Reddit is full of people describing problems out loud. Founders ask what others are building, site owners ask why Google is ignoring their pages, and small business owners ask for help they cannot find elsewhere.
TL;DR: Pick a gateway for moderation classification by the evidence it preserves, not by how many model names fit behind one key. Require every provider path to produce the same small JSON contract, validate it before accepting the result, and record why routing or fallback happened.
Tumne notice kia? #coding #programming #dots #muse #ai 👉About This Video: This video has been independently edited, redesigned, and transformed. It contains original commentary, analysis, and educational insights added for the purpose of learning and understanding.
A team compares two versions of an AI service. Average accuracy rises from 82% to 86%. The new version looks better, so releasing it seems like the obvious decision. Then an engineer separates the failures by type.
Originally published on tamiz.pro. You added another 2,000 tokens of coding conventions to your system prompt. Your agent now follows the style guide... but it also ignores your bug-fix instructions, hallucinates import paths, and produces function bodies that are 40% longer than necessary.
Should you trade with Jev? Not on price data, not yet. I haven't found anyone who has shown it beats a simple model at reading a market, and in my own test it read Bitcoin's indicators the wrong way round.
When two diplomats sat down to negotiate a fragile ceasefire in 2024, neither realized the "neutral" AI translation tool they relied on had been quietly shaped by the geopolitical priorities of the country that built it. The words were translated correctly. The emphasis was not.
Can Jev predict Bitcoin? On the test I ran, no. I sent Jev the same 12 technical indicators that a plain gradient-boosting model used, for 1,500 BTCUSDT 5-minute candles neither had seen. Jev answered the control questions correctly 99.8% of the time, so it read the numbers fine.
Somewhere in a roleplay community, people are feeding their own chat replies into a model built for industrial automation. The model scores their writing and tells them which reply sounds more in character. That model is Jev, and it is not what TypeSafe AI put in its press release.
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