Show HN: A Kubernetes Operator to Validate Jupyter Notebooks in MLOps https://bit.ly/4rAX8tG

Show HN: A Kubernetes Operator to Validate Jupyter Notebooks in MLOps I built an open-source Kubernetes operator to automate the validation of Jupyter Notebooks in MLOps workflows. It's called the Jupyter Notebook Validator Operator and it's designed to catch issues with notebooks before they hit production. It runs notebooks in isolated pods and can validate them against deployed ML models on platforms like KServe, OpenShift AI, and vLLM. It also does regression testing by comparing notebook outputs against a "golden" version. The goal is to make notebooks more reliable and reproducible in production environments. It's built with Go and the Operator SDK. We're looking for contributors. There are opportunities to work on features like smarter error reporting, observability dashboards, and adding support for more platforms. GitHub: https://bit.ly/3ObO3Jf... https://bit.ly/46dId0r February 7, 2026 at 01:10AM

Show HN: Falcon's Eye (isometric NetHack) running in the browser via WebAssembly https://bit.ly/3O1GwNh

Show HN: Falcon's Eye (isometric NetHack) running in the browser via WebAssembly https://bit.ly/3MaBiOI February 6, 2026 at 11:19PM

Show HN: Calfkit – an SDK to build distributed, event-driven AI agents https://bit.ly/4ru0u1n

Show HN: Calfkit – an SDK to build distributed, event-driven AI agents I think agents should work like real teams, with independent, distinct roles, async communication, and the ability to onboard new teammates or tools without restructuring the whole org. I built backend systems at Yahoo and TikTok so event-driven agents felt obvious. But no agent SDKs were using this pattern, so I made Calfkit. Calfkit breaks down agents into independent services (LLM inference, tools, and routing) that communicate asynchronously through Kafka. Agents, tool services, and downstream consumers can be deployed, added-to, removed, and scaled independently. Check it out if this interests you! I’m curious to see what y’all think. https://bit.ly/4tlDXpq February 6, 2026 at 12:10AM

Show HN: Total Recall – write-gated memory for Claude Code https://bit.ly/4rbjqCr

Show HN: Total Recall – write-gated memory for Claude Code https://bit.ly/4rbjqSX February 6, 2026 at 12:56AM

Show HN: A state-based narrative engine for tabletop RPGs https://bit.ly/3ZoN2Qy

Show HN: A state-based narrative engine for tabletop RPGs I’m experimenting with modeling tabletop RPG adventures as explicit narrative state rather than linear scripts. Everdice is a small web app that tracks conditional scenes and choice-driven state transitions to preserve continuity across long or asynchronous campaigns. The core contribution is explicit narrative state and causality, not automation. The real heavy lifting is happening in the DM Toolkit/Run Sessions area, and integrates CAML (Canonical Adventure Modeling Language) that I developed to transport narratives among any number of platforms. I also built the npm CAML-lint to check validity of narratives. I'm interested in your thoughts. https://bit.ly/4rstuXo https://bit.ly/4khOu0B February 5, 2026 at 11:55PM

Show HN: LLM Jailbreak Database https://bit.ly/45MlF6B

Show HN: LLM Jailbreak Database I vibe-coded this online DB for LLM injection prompts. It's registration/login less with some ambitious spam/bot filtering. I'm interested in trying to tune the barriers of interaction to a sweet spot where the DB gets balanced and the useful working injections are actually on top. thoughts? https://bit.ly/3ZiszwQ February 4, 2026 at 11:07PM

Show HN: Bunqueue – Job queue for Bun using SQLite instead of Redis https://bit.ly/4qgqo7S

Show HN: Bunqueue – Job queue for Bun using SQLite instead of Redis https://bit.ly/46oMxtB February 2, 2026 at 02:55AM

Show HN: The Last Worm – Visualizing guinea worm eradication, from 3.5M to 10 https://bit.ly/4klzH5l

Show HN: The Last Worm – Visualizing guinea worm eradication, from 3.5M to 10 https://bit.ly/3Mn1A08 February 4, 2026 at 11:58PM

Show HN: Craftplan – I built my wife a production management tool for her bakery https://bit.ly/4rv9Aeq

Show HN: Craftplan – I built my wife a production management tool for her bakery My wife was planning to open a micro-bakery. We looked at production management software and it was all either expensive or way too generic. The actual workflows for a small-batch manufacturer aren't that complex, so I built one and open-sourced it. Craftplan handles recipes (versioned BOMs with cost rollups), inventory (lot traceability, demand forecasting, allergen tracking), orders, production batch planning, and purchasing. Built with Elixir, Ash Framework, Phoenix LiveView, and PostgreSQL. Live demo: https://bit.ly/3O3vU0c (test@test.com / Aa123123123123) GitHub: https://bit.ly/4ryZ6ec https://bit.ly/4ryZ6ec February 1, 2026 at 06:25PM

Show HN: I built an AI twin recruiters can interview https://bit.ly/4rq48t9

Show HN: I built an AI twin recruiters can interview https://bit.ly/4aedLV6 The problem: Hiring new grads is broken. Thousands of identical resumes, but we're all different people. Understanding someone takes time - assessments, phone screens, multiple interviews. Most never get truly seen. I didn't want to be just another PDF. So I built an AI twin that recruiters can actually interview. What you can do: •Interview my AI about anything: https://bit.ly/4rmCT2A •Paste your JD to see if we match: https://bit.ly/4rojOgy •Explore my projects, code, and writing What happened: Sent it to one recruiter on LinkedIn. Next day, traffic spiked as it spread internally. Got interview invites within 24 hours. The bigger vision: What if this became standard? Instead of resume spam → keyword screening → interview rounds that still miss good fits, let recruiter AI talk to candidate AI for deep discovery. Build a platform where anyone can create their AI twin for genuine matching. I'm seeking Software/AI/ML Engineering roles and can build production-ready solutions from scratch. The site itself proves what I can do. Would love HN's thoughts on both the execution and the vision. https://bit.ly/4aedLV6 February 4, 2026 at 12:19AM

Show HN: Axiomeer – An open marketplace for AI agents https://bit.ly/4aykLfJ

Show HN: Axiomeer – An open marketplace for AI agents Hi, I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything. How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations required? timestamps?), and evidence quality is assessed deterministically - If the evidence is mock/fake/low-quality, the agent abstains rather than hallucinating - Every execution is logged as an immutable receipt The trust layer is the part I think is missing from existing approaches. MCP standardizes how you connect to a tool server. Axiomeer operates one layer up: which tool, from which provider, and can you trust what came back? Stack: Python, FastAPI, SQLAlchemy, Ollama (local LLM, no API keys). v1 ships with weather providers (Open-Meteo + mocks). The architecture supports any HTTP endpoint that returns structured JSON. Looking for contributors to add real providers across domains (finance, search, docs, code execution). Each provider is ~30 lines + a manifest. https://bit.ly/4byfTsV February 3, 2026 at 01:43AM

Show HN: Kannada Nudi Editor Web Version https://bit.ly/4aclQcT

Show HN: Kannada Nudi Editor Web Version Ported the Desktop Version of Kannada Nudi Editor to Web under the guidance of https://bit.ly/4a4vbER https://bit.ly/49W412S February 3, 2026 at 05:11AM

Show HN: Stream-based AI with neurological multi-gate (Na⁺/θ/NMDA) https://bit.ly/49UI6cb

Show HN: Stream-based AI with neurological multi-gate (Na⁺/θ/NMDA) Current LLMs struggle with compositional inference because they lack physical boundaries. CSCT implements a neurological multi-gate mechanism (Na⁺/θ/NMDA) to enforce L1 geometry and physical grounding. In my experiments (EX8/9), this architecture achieved 96.7% success in compositional inference within the convex hull—far outperforming unconstrained models.Key features:Stream-based: No batching or static context; it processes information as a continuous flow.Neurological Gating: Computational implementation of θ-γ coupling using Na⁺ and NMDA-inspired gates.Zero-shot Reasoning: Incurs no "hallucination" for in-hull compositions.Detailed technical write-up: [ https://bit.ly/4kds5BD... ]I’m eager to hear your thoughts on this "Projected Dynamical System" approach to cognition. https://bit.ly/4kds5S9 February 3, 2026 at 03:59AM

Show HN: 127 PRs to Prod this wknd with 18 AI agents: metaswarm. MIT licensed https://bit.ly/3Ois03A

Show HN: 127 PRs to Prod this wknd with 18 AI agents: metaswarm. MIT licensed A few weeks ago I posted about GoodToGo https://bit.ly/4pI0dXu - a tool that gives AI agents a deterministic answer to "is this PR ready to merge?" Several people asked about the larger orchestration system I mentioned. This is that system. I got tired of being a project manager for Claude Code. It writes code fine, but shipping production code is seven or eight jobs — research, planning, design review, implementation, code review, security audit, PR creation, CI babysitting. I was doing all the coordination myself. The agent typed fast. I was still the bottleneck. What I really needed was an orchestrator of orchestrators - swarms of swarms of agents with deterministic quality checks. So I built metaswarm. It breaks work into phases and assigns each to a specialist swarm orchestrator. It manages handoffs and uses BEADS for deterministic gates that persist across /compact, /clear, and even across sessions. Point it at a GitHub issue or brainstorm with it (it uses Superpowers to ask clarifying questions) and it creates epics, tasks, and dependencies, then runs the full pipeline to a merged PR - including outside code review like CodeRabbit, Greptile, and Bugbot. The thing that surprised me most was the design review gate. Five agents — PM, Architect, Designer, Security, CTO — review every plan in parallel before a line of code gets written. All five must approve. Three rounds max, then it escalates to a human. I expected a rubber stamp. It catches real design problems, dependency issues, security gaps. This weekend I pointed it at my backlog. 127 PRs merged. Every one hit 100% test coverage. No human wrote code, reviewed code, or clicked merge. OK, I guided it a bit, mostly helping with plans for some of the epics. A few learnings: Agent checklists are theater. Agents skipped coverage checks, misread thresholds, or decided they didn't apply. Prompts alone weren't enough. The fix was deterministic gates — BEADS, pre-push hooks, CI jobs all on top of the agent completion check. The gates block bad code whether or not the agent cooperates. The agents are just markdown files. No custom runtime, no server, and while I built it on TypeScript, the agents are language-agnostic. You can read all of them, edit them, add your own. It self-reflects too. After every merged PR, the system extracts patterns, gotchas, and decisions into a JSONL knowledge base. Agents only load entries relevant to the files they're touching. The more it ships, the fewer mistakes it makes. It learns as it goes. metaswarm stands on two projects: https://bit.ly/465Uggf by Steve Yegge (git-native task tracking and knowledge priming) and https://bit.ly/4tg1fwL by Jesse Vincent (disciplined agentic workflows — TDD, brainstorming, systematic debugging). Both were essential. Background: I founded Technorati, Linuxcare, and Warmstart; tech exec at Lyft and Reddit. I built metaswarm because I needed autonomous agents that could ship to a production codebase with the same standards I'd hold a human team to. $ cd my-project-name $ npx metaswarm init MIT licensed. IANAL. YMMV. Issues/PRs welcome! https://bit.ly/4tcbDpg February 3, 2026 at 02:18AM

Show HN: ContractShield – AI contract analyser for freelancers https://bit.ly/463VwR1

Show HN: ContractShield – AI contract analyser for freelancers Built this with Claude Code. Analyses freelance contracts for 12 risk categories (payment terms, IP ownership, scope issues, termination clauses, etc.) and flags problems with specific recommendations. 40% of freelancers report getting stiffed by clients, often due to vague contract terms. This tool aims to help catch those issues before signing. Currently free while validating whether this solves a real problem. Would love HN's feedback, especially on: - Accuracy of the analysis - Whether this is actually useful for freelancers - What's missing or could be improved Tech stack: Node.js, Express, Anthropic Claude API, deployed on Railway. https://bit.ly/3ZcvXJv February 2, 2026 at 04:11AM

Show HN: Is AI "good" yet? – tracking HN sentiment on AI coding https://bit.ly/4qfxfhU

Show HN: Is AI "good" yet? – tracking HN sentiment on AI coding A survey tracking developer sentiment on AI-assisted coding through Hacker News posts. https://bit.ly/4q7Kp0j February 2, 2026 at 03:06AM

Show HN: Wikipedia as a doomscrollable social media feed https://bit.ly/3Oj4jbm

Show HN: Wikipedia as a doomscrollable social media feed https://bit.ly/4rj1aXw February 2, 2026 at 01:12AM

Show HN: NanoClaw – “Clawdbot” in 500 lines of TS with Apple container isolation https://bit.ly/4qau5fm

Show HN: NanoClaw – “Clawdbot” in 500 lines of TS with Apple container isolation I’ve been running Clawdbot for the last couple weeks and have genuinely found it useful but running it scares the crap out of me. OpenClaw has 52+ modules and runs agents with near-unlimited permissions in a single Node process. NanoClaw is ~500 lines of core code, agents run in actual Apple containers with filesystem isolation. Each chat gets its own sandboxed context. This is not a swiss army knife. It’s built to match my exact needs. Fork it and make it yours. https://bit.ly/4qTY7oY February 1, 2026 at 11:49PM

Show HN: Peptide calculators ask the wrong question. I built a better one https://bit.ly/4r36xtW

Show HN: Peptide calculators ask the wrong question. I built a better one Most peptide calculators ask the wrong question. They ask: How much water are you adding? But in practice, what you actually know is your vial size and your target dose . The water amount should be the output , not the input . It should also make your dose land on a real syringe tick mark. Not something like 17.3 units. I built a peptide calculator that works this way: https://bit.ly/4r36y0Y What’s different: - You pick vial size and target dose → reconstitution is calculated for you - Doses align to actual syringe markings - Common dose presets per peptide - Works well on mobile (where this is usually done) - Supports blends and compounds (e.g. GLOW or CJC-1295 + Ipamorelin) - You can save your vials. No account required. Happy to hear feedback or edge cases worth supporting. https://bit.ly/4r36y0Y February 1, 2026 at 03:02AM

Show HN: I built a receipt processor for Paperless-ngx https://bit.ly/4tcdRFa

Show HN: I built a receipt processor for Paperless-ngx Hi all, I wanted a robust way to keep track of my receipts without needing to keep them in a box and so i found paperless - but the existing paperless ai projects didn't really convert my receipts to usable data. so I created a fork of nutlope's receipthero (actually it's a complete rewrite, the only thing that remains over is the system prompt) The goal of this project is to be a one stop shop for automatically detecting tagged docs and converting them to json using schema definitions - that includes invoices, .... i can't think of any others right now, maybe you can? If you do please make an issue for it! I would appreciate any feedback/issues thanks! (p.s i made sure its simple to setup with dockge/basic docker-compose.yml) repo: https://bit.ly/4a61i5v tutorial: https://youtu.be/LNlUDtD3og0 February 1, 2026 at 01:17AM