Show HN: Unlock the best engineering knowledge in papers for your coding agent https://ift.tt/7Lap5hD

Show HN: Unlock the best engineering knowledge in papers for your coding agent https://ift.tt/tFH29RZ February 23, 2026 at 11:03PM

Show HN: AgentDbg - local-first debugger for AI agents (timeline, loops, etc.) https://ift.tt/X47Ybwd

Show HN: AgentDbg - local-first debugger for AI agents (timeline, loops, etc.) AgentDbg is a local-first debugger for AI agents. It records structured runs (LLM calls, tool calls, state, errors) to JSONL and shows the timeline UI locally. There is no need for cloud, accounts, and no telemetry. Flow is as simple as: 1. Run an agent 2. `agentdbg view` 3. Inspect the timeline, loop warnings, errors, etc. v0.1 includes `@trace` and `traced_run`, recorders, loop detection, best-effort redaction (by default), local UI, export. I also started working on integrations: there is an optional LangChain/LangGraph callback. * Repo: https://ift.tt/TiKuMba * Demo: `python examples/demo/pure_python` and then `agentdbg view` Would love feedback on: 1. Trace format 2. Integrations to prioritize in the next several days 3. What you would want for deterministic replay https://ift.tt/TiKuMba February 23, 2026 at 11:14PM

Show HN: Seafruit – Share any webpage to your LLM instantly https://ift.tt/aGcIBw7

Show HN: Seafruit – Share any webpage to your LLM instantly Hi HN, This weekend I built seafruit.pages.dev to privately share any webpage with my LLM. More sites are (rightfully) blocking AI crawlers but as a reader with the page already open, it's frustrating that my AI assistant can't "see" what I'm already reading. One click → clean Markdown → copied to clipboard. No extension, no tracking. Existing solutions like AI browsers or extensions felt too intrusive. I wanted something surgical, fast, and private. How it works: It's a bookmarklet. Click it on any page → it extracts clean text as Markdown → copies an AI-optimized link to your clipboard. No extension needed. Key details: Zero friction: Drag the bookmark to your bar. Works on mobile too. Privacy-first: Links are ephemeral (24 hrs on Free). PRO links self-destruct the moment an AI bot finishes reading them. LLM-optimized: Clean Markdown, not raw HTML — no wasted context window. Fast everywhere: Built on Cloudflare Workers. Would love feedback on the workflow or ideas for other anti-friction features. https://seafruit.pages.dev P.S. Thanks to mods Daniel and Tom for helping me recover my account! https://seafruit.pages.dev February 22, 2026 at 11:41PM

Show HN: Winslop – De-Slop Windows https://ift.tt/paXd9MV

Show HN: Winslop – De-Slop Windows https://ift.tt/Qrt50hj February 22, 2026 at 01:26AM

Show HN: Rigour – Open-source quality gates for AI coding agents https://ift.tt/oxlhc5a

Show HN: Rigour – Open-source quality gates for AI coding agents Hey HN, I built Rigour, an open-source CLI that catches quality issues AI coding agents introduce. It runs as a quality gate in your workflow — after the agent writes code, before it ships. v4 adds --deep analysis: AST extracts deterministic facts (line counts, nesting depth, method signatures), an LLM interprets what the patterns mean (god classes, SRP violations, DRY issues), then AST verifies the LLM didn't hallucinate. I ran it on PicoClaw (open-source AI coding agent, ~50 Go files): - 202 total findings - 88 from deep analysis (SOLID violations, god functions, design smells) - 88/88 AST-verified (zero hallucinations) - Average confidence: 0.89 - 120 seconds for full codebase scan Sample finding: pkg/agent/loop.go — 1,147 lines, 23 functions. Deep analysis identified 5 distinct responsibilities (agent init, execution, tool processing, message handling, state management) and suggested specific file decomposition. Every finding includes actionable refactoring suggestions, not just "fix this." The tool is local-first — your code never leaves your machine unless you explicitly opt in with your own API key (--deep -k flag). Tech: Node.js CLI, AST parsing per language, structured LLM prompts with JSON schema enforcement, AST cross-verification of every LLM claim. GitHub: https://ift.tt/3qHyYEr Would love feedback, especially from anyone dealing with AI-generated code quality in production. https://rigour.run February 21, 2026 at 10:45PM

Show HN: Manifestinx-verify – offline verifier for evidence bundles (drift) https://ift.tt/ta9Gu0O

Show HN: Manifestinx-verify – offline verifier for evidence bundles (drift) Manifest-InX EBS is a spec + offline verifier + proof kit for tamper-evident evidence bundles. Non-negotiable alignment: - Live provider calls are nondeterministic. - Determinism begins at CAPTURE (pinned artifacts). - Replay is deterministic offline. - Drift/tamper is deterministically rejected. Try it in typically ~10 minutes (no signup): 1) Run the verifier against the included golden bundle → PASS 2) Tamper an artifact without updating hashes → deterministic drift/tamper rejection Repo: https://ift.tt/rAOXmKd Skeptic check: docs/ebs/PROOF_KIT/10_MINUTE_SKEPTIC_CHECK.md Exit codes: 0=OK, 2=DRIFT/TAMPER, 1=INVALID/ERROR Boundaries: - This repo ships verifier/spec/proof kit only. The Evidence Gateway (capture/emission runtime) is intentionally not included. - This is not a “model correctness / no hallucinations” claim—this is evidence integrity + deterministic replay/verification from pinned artifacts. Looking for feedback: - Does the exit-code model map cleanly to CI gate usage? - Any spec/report format rough edges that block adoption? https://ift.tt/rAOXmKd February 20, 2026 at 11:57PM

Show HN: HelixDB Explorer – A macOS GUI for HelixDB https://ift.tt/pEBko8w

Show HN: HelixDB Explorer – A macOS GUI for HelixDB https://ift.tt/EM3hjoC February 20, 2026 at 11:18PM