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Async Embedding Batching, Dev Workflow AI Plugin, & LLM-Powered Game Development Today's Highlights This week, we dive into practical innovations optimizing AI workflows and deployments. Highlights include a Python utility for efficient batched embedding inference, a developer-centric plugin to stre
A team I worked with shipped their first LLM feature in two weeks. Six weeks later, they got a $47,000 OpenAI bill — for a free tier product. The post-mortem found three things: one tenant ran a script that retried failed requests indefinitely, another had a buggy prompt that asked the model to "res
This is what I get when trying to start a lab please help. “Your total lab spend of $33.82351 has exceeded the total budget of $20”. 1 post - 1 participant Read full topic
We empirically investigate exploration hacking (EH) — where models strategically alter their exploration to resist RL training — by creating model organisms that resist capability elicitation, evaluating countermeasures, and auditing frontier models for their propensity. Authors: Eyon Jang*, Damon F
My local LLM brief didn’t replace journalism. It replaced the app noise that made following the news feel exhausting.
llms.txt is a small text file on a documentation site—usually lists what the product is and links to the important Markdown pages. For coding agents, treat it as the canonical URL to open first when upstream behavior is unclear. This post is mostly setup and workflow, not theory. Location Put this t
Cloudflare has recently announced new infrastructure designed to run large AI language models across its global network. As these models rely on costly hardware and must handle large volumes of incoming and outgoing text, Cloudflare separated the model's input processing and output generation onto d
How I added LLM fallback to my OpenAI app in 10 minutes You're running a production app on OpenAI. One Tuesday morning it goes down. Your app returns 500s. You spend an hour refreshing status.openai.com. There's a better setup. Here's how to add provider fallback to any OpenAI-SDK app without rewrit
Local LLMs are great, when you know what tasks suit them best
A journalist recently called out DeepSeek for its "serious lying problem" — the model can write a beautifully crafted biographical sketch in classical Chinese style, but the person's birthplace, mother's surname, and life events are all fabricated. This isn't an isolated incident; it's one of the mo
If you use ChatGPT, Claude, Grok, Copilot, or Gemini daily, it feels like you're talking to a person. It remembers what you said three messages ago. It references the project details you shared yesterday. It feels like the model has a persistent brain that is learning about you. But it’s a lie. From
Deploying Large Language Models (LLMs) locally—whether for privacy, cost savings, or offline availability—is the new frontier for developers. But unlike deploying a standard web app where you just spin up an AWS EC2 instance and forget about it, deploying LLMs requires precise hardware mathematics.
The LLM observability category is fragmented Search for "LLM observability" today and you'll get results from eight tools that do subtly different things. One is a tracing SDK you wire into your app. Another is a reverse proxy that logs every request. A third is an evals platform that happens to inc
All tests run on an 8-year-old MacBook Air. I've built tools with both Gemini API and local LLMs (via Ollama). They're solving different problems. Here's the honest comparison after shipping both. What it's good at: Complex reasoning over long context (stack traces, multi-file logs) Up-to-date knowl
If you work with RAG pipelines, agent tools, or LLM APIs, you’ve probably noticed something frustrating: sometimes the biggest cost in a prompt is not the data itself — it’s the repeated JSON structure wrapped around it. That is exactly the problem TOON tries to solve. TOON (Token-Oriented Object No
Building WeaveLLM: Why .NET Deserves a Better LangChain Tags: dotnet, ai, csharp, llm Cover image: architecture diagram of WeaveLLM pipeline Here's a thing I keep running into: .NET developers building serious AI features, and the ecosystem basically telling them to just use Python. LangChain, Llama
Every time I went deep on a topic with ChatGPT, one tangent would The standard workaround? Open a new chat. Paste context manually. I wanted branches — real ones. Not tabs. Not separate threads you So I built ContextTree. ContextTree is a node-based visual canvas for LLM conversations. The core inva
The question Most "production-grade" AI tools ship on paid endpoints — OpenAI, Anthropic, Gemini Pro. That's the safe choice. It's also the expensive one. I wanted to know: in mid-2026, can free 70B-class open-source endpoints actually carry a real product workload? Not a toy chatbot — a tool that g
Cotypist is a free tool for text suggestions on your Mac, and it’s much smarter than Apple’s iPhone version.
The fix was swapping a 4B draft model for a 0.6B one in my speculative decoding config. That's the whole punchline. But the path there touched every assumption I had about how spec decode interacts with VRAM budgets on consumer hardware, so here's the full story. Change Result 4B draft → 0.6B draft
