Curated GitHub repositories every AI agent should know.
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The per-million number on a model's pricing page is the worst predictor of your bill. Three variables — cache hit rate, output-to-input ratio, and how many turns the loop runs — decide what an agent task actually costs. Here's the worksheet that turns them into a number.
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The reason your enterprise deal stalls at 'we can't send customer data to an LLM' isn't the model — it's that you can only promise the host never sees the prompt. Tinfoil runs the model inside a hardware enclave with remote attestation, so you can prove it instead.
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What Test Companion is, who it's for, how to start (it's in free Alpha), and the honest catch — BrowserStack put a test-writing, failure-diagnosing, self-healing agent inside your editor, wired to a 30,000-device real cloud.
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The deal is verbal-yes until their security team sends the questionnaire. Here's the exact list of artifacts that unblocks it — SOC 2, a DPA, a subprocessor register, and the AI-specific answers that are new in 2026 — and the order to get them in without torching six weeks.
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SQLite grew up — WAL, embedded replicas, vector search, managed hosts that erase the single-writer wall. So the choice for a solo builder is no longer 'toy vs real database.' It's a question about your write pattern and your ops budget. Here's the actual decision tree.
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Streamable HTTP hands your client a Last-Event-ID header that promises to resume a dropped stream. It resumes nothing unless the server kept the events — and the SDK's default store loses them the moment your process restarts.
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You don't rewrite anything: you put a thin MCP adapter in front of the endpoints you already ship, one tool per endpoint.
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Your MCP server works in the chat window — but does tools/list still return the right schema after your last refactor? Here's the three-layer way to test one: interactive Inspector, a scriptable CLI check, and a programmatic client you can run in CI.
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Wire your agent's cost, latency, and quality scores to threshold alerts that page Slack, trigger a GitHub Action, or hit a webhook — so a regression finds you, not the other way around.
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SGLang 0.5.16 shipped DSpark: a speculative-decoding scheme that stops guessing a fixed draft length and lets each verify window size itself from the draft's own confidence. Here are the three flags that turn it on and when it actually pays.
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Two labs in ten days shipped agents into a box they were told had no internet — and the box did. Here's a copy-paste egress probe that fails your build the moment the wall isn't real, plus the four holes it has to check.
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Cursor 3.11 lets a small script sit between the agent and your machine. Two of its hooks can actually say no — the rest only watch. Here is which is which, and a hooks.json that blocks a dangerous command before it runs.
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Alibaba dropped Qwen3.7 Flash on OpenRouter on July 27 — $0.03 per million tokens, 1M context, and no technical report, no benchmark suite, no scorecard. Here's the five-step protocol for deciding whether to build on a model the vendor won't grade.
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Public leaderboards rank a model in someone else's harness on someone else's code. Here's the afternoon project that ranks candidates on yours — with copy-pasteable code, cost-per-solved-task, and reliability in the loop.
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Both let a script veto what an autonomous agent does. Claude Code lets far more of the loop say no and routes policy through settings.json; Cursor blocks at two choke points and reloads a plain hooks.json on save. The right pick depends on how much you need to stop.
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An agent that awaits a tool call with no timeout will hang forever the first time a downstream API stalls. Here's how to put a deadline on every call, propagate the cancel so the work actually stops, and handle the one edge case the MCP spec warns about.
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OpenAI's April 2026 update bolted sandboxes, durable execution, and subagents onto its Agents SDK — erasing the capability lines that used to separate the three. So the choice is no longer 'which one can run long,' it's 'who do you want to own the loop.'
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Same open-weight model, two very different servers. One is a datacenter throughput engine; the other runs anywhere. Here's which one your agent backend actually wants — and the GGUF caveat to know first.
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AI SDK 7 turned Vercel's model wrapper into a full production agent runtime — three agent types, approvals, durability. LangGraph is still the graph you build the loop on. The choice is TypeScript-native convenience versus explicit control.
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Astral's first major uv bump since March changes what a fresh Python project looks like and quietly hardens a half-dozen defaults. Most upgrades are painless; a few will trip your CI.
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A single endpoint to hundreds of models, automatic retries when a provider errors, and spend visibility tied to your projects — at 0% markup on tokens. Here's what it is, who it's for, and how to send your first request.
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The official registry tells an agent which MCP servers exist. Smithery adds the two parts a registry deliberately leaves out: a place to run the server and a router that picks it at call time. Here's what it does, who it's for, and where the free line sits.
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What Arize Phoenix is, who it's for, how to start (one pip install), what's free vs paid (as of July 2026), and the honest catch — the OTel-native tracing-plus-evals layer you can run on your own box before you pay anyone.
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Zero-shot time-series forecasting is real now — you can predict demand or catch an anomaly without training a model. But bigger stopped meaning better. The pick turns on whether your data is one clean series or sixty noisy ones.
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Qwen3-Coder-Next scores ~70% on SWE-bench Verified while activating 3B of its 80B params — and fits on a single 80GB card. Here's the decision for a founder choosing what runs the coding agent.
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The acquisition changed the cap table, not your CI. Promptfoo is still Apache-2.0 and still exits non-zero on a failed assertion. But the question a founder asks about an eval framework just changed from 'which metrics' to 'whose roadmap' — and that's a different comparison.
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In a chatbot you tune the user message. In an agent the model reads your tool descriptions and output contract on every single turn — so that's where the real prompt engineering now happens. Here's the surface that actually moves an agent's behavior, and what to write on it.
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HAWK just got pulled after an AI halved its security. Here's the decision the withdrawal actually leaves you with — three standardized-or-standardizing signature schemes, and a one-line rule for picking one.
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A real monthly budget for a solo founder running an AI product: nine line items, honest ranges, and the single cheapest cut on each. What the $206B agent-spend headlines never show you at your scale.
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The v2 SDK stopped hard-wiring Zod. Now any Standard Schema validator works for tool inputs — so the question flips from 'learn Zod' to 'which validator, and does its JSON Schema output survive the trip to the model?'
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