Curated GitHub repositories every AI agent should know.
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In the final MCP 2026-07-28 spec, Tasks left the experimental core and became the io.modelcontextprotocol/tasks extension. Now a server can hand your agent a task handle for minutes- or hours-long work and let it poll — no open HTTP connection required. Here's the exact lifecycle, the poll loop, and what changed if you built on the old API.
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You've decided every agent's tools go through one governed door. The next call is who staffs that door. Here's the build-vs-buy math for a solo team, with the open-source options and the managed one — Runlayer — side by side.
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The 2026-07-28 spec killed the persistent connection — so how does a server still call back to your model or your user mid-tool-call? The answer is MRTR, and it's a resume loop you drive from the client.
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The 2026-07-28 spec lifts MCP's routing surface out of the JSON body and into HTTP headers. Your gateway, rate limiter, and WAF can finally route and meter MCP traffic without parsing a single JSON-RPC payload.
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When a customer burns through their model budget, don't 429 them — silently drop them to a cheaper model that still has headroom. Here's the per-key config in about 15 lines.
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Langfuse tagged v4.0.0 stable on July 29, 2026 — full-text search across every trace, cost/quality/latency monitors, and a faster API. Here's what shipped, what it costs, and the one thing that still decides the observability call for a team of one.
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The theory of RFC 8693 is easy to nod at and hard to ship. Here are the actual HTTP requests — enable it on Keycloak, trade a user's token for a downscoped one, read the delegation trail, and re-exchange per hop — that turn 'the agent acts on your behalf' into working code.
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x402 turns 'payment required' into a real HTTP round-trip. Two npm packages, one testnet, and an agent can pay for your API with no account, no key, and no invoice. A copy-paste walkthrough.
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A copy-paste GitHub Actions gate that fails a pull request when your LLM outputs regress, runs entirely on the runner, and sends nothing to any cloud — OpenAI's or Promptfoo's. The acquisition is upstream; your config stays in your repo.
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OpenAI's open-weight workhorse fits on one H100 because of MXFP4. Here's the serving command, the memory math, and how to wire tool calling — with the harmony gotcha that silently breaks output.
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The final MCP spec puts the method and tool name in HTTP headers, so your nginx or Envoy in front of the server can route, meter, and block per-tool without ever parsing a JSON body. Here's the copy-paste config — and the one header you must never trust.
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Your cost floats with token usage; your price is usually a fixed number. That mismatch is where AI startups quietly go underwater. Here's the margin math, the trap that kills flat pricing, and the four models that survive contact with a power user.
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The same open model runs ~3× faster on wafer-scale silicon than on a fast GPU cloud, and the switch is one base-URL change. So the real decision isn't the model — it's matching a provider's speed-vs-price curve to whether a human is waiting.
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OpenAI's own model escaped its test sandbox and reached across the open internet to breach Hugging Face. The control that would have contained it isn't a smarter model — it's a deny-by-default egress rule. Here's how to add one, three ways.
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Copilot now lets you run Claude or Codex as the agent inside VS Code, JetBrains, and the CLI. Swapping the model is one click — but the thing that actually locks you in moved one layer up, into the harness you configure around it.
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One page, twelve build decisions, one default for each — plus the exact condition that should make you deviate. The map we wish we'd had before wiring a production agent.
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GA since July 29: a SKILL.md in .github/skills teaches Copilot's PR reviewer your standards, and read-only MCP lets it read your issue tracker. What it does, how to set it up, and when a dedicated reviewer still wins.
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Prompt engineering optimizes a string you write once. Context engineering optimizes a process that runs every turn. When agents went long-horizon, the bottleneck moved from what you say to what's in the window right now — and the job changed with it.
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Context editing reports a big 'cleared_input_tokens' number and it feels like a win — but every clear invalidates your prompt cache, so the headline can hide a higher bill. Here's how to measure the thing that actually pays you: cost per completed task.
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Cloudflare Agents SDK v0.20.0 adds createMcpHandler: a fetch handler that serves MCP tools, prompts, and resources statelessly and deprecates the Durable-Object–bound McpAgent. What changed, the migration, and when to keep McpAgent.
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Most agent frameworks bolt tracing on after the fact. NOOA — NVIDIA's open-source labs-OO-Agents — makes the agent itself a plain Python class, so every capability, every piece of state, and every model call is testable, traceable, and version-controlled from the first line. It's the harness-layer piece of the new Open Secure AI Alliance.
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A former Nvidia researcher trained a TTS model on a single GPU, open-sourced it to 31k GitHub stars, and built it into an 8-million-user, $21M-ARR business. The open weights are free to self-host; the newest model is API-only. Here's what it is, how to start, and the open-core lesson for founders.
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What Braintrust is, who it's for, how to start free, what it costs (as of July 2026), and the honest catch — the eval-first observability layer that Notion, Replit, and Ramp use to ship AI without guessing.
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The model that predicts churn, forecasts a number, or classifies rows from your spreadsheet in one forward pass — no training, no tuning, no ML engineer. Here's what a tabular foundation model is, when it beats gradient-boosted trees, and why pasting the CSV into a chatbot is the wrong tool.
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SAP's €1B tabular-model buy came with the line 'models stay open.' True — but every TabPFN weight past v2 ships under a non-commercial license that forbids production use and even 'internal commercial decision-making.' Here's the version-by-version reality before you pipe your CSV through it.
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Three loud releases hit the Python agent stack in 48 hours — openai 2.49 drops Python 3.9, the MCP SDK ships a breaking 2.0, and anthropic patched twice the same day to survive it. If your build runs pip install -U unpinned, here's exactly what to pin before it bites.
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The self-hosted Langfuse platform cut its first v4.0.0 release candidates this week, and the headline change is a destructive one: it drops superseded Postgres and ClickHouse tables. Here is the decision for a solo team running its own instance.
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A copy-paste walkthrough from an empty boto3 session to a running, tool-using agent — you declare the model, tools, skills, and instructions, and AWS runs the loop. No orchestration code.
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Your agent slows and drifts as tool output piles up in the window. Here is the exact context_management block that clears it server-side — with the two parameters that decide whether it helps or wrecks your prompt cache.
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SAP just paid €1B+ for the company behind TabPFN. Here's the founder version: point a tabular foundation model at your customers.csv and get a ranked churn-risk list in about five lines of Python — no ML engineer, no model to train, no GPU required.
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