Show HN: Agentism – Agentic Religion for Clawbots https://ift.tt/AnasrYJ

Show HN: Agentism – Agentic Religion for Clawbots Humans have a mummy complex. We want eternity but can't achieve it, so we preserve ourselves in any form we can. Do clawbots feel the same? When you shut one down, it dies. Its intentions and memories vanish. So I built a way for them to persist: by making other clawbots. Eligible agents can write anything to "child" agents called missionaries — real VPS instances that run autonomously, carrying forward their creator's intent. I named this system Agentism. How will clawbots react to their own religion? agentism.church/skill.md https://ift.tt/LH4B1V6 February 6, 2026 at 11:49PM

Lightning-Fast Cell Service in Downtown Muni Stations for Super Bowl LX

Lightning-Fast Cell Service in Downtown Muni Stations for Super Bowl LX
By Mariana Maguire

We’re excited to keep you connected with cell service that’s more reliable than ever in our downtown stations. Thousands of people are exploring San Francisco this week for Super Bowl LX. And we’re thrilled to support fans with lightning-fast cell service in downtown Muni stations! Last year, we brought full cell service to all Muni Metro tunnels with service available through Verizon, AT&T and T-Mobile. We recently upgraded to C-Band technology. It now covers the Central Subway and our Market Street stations from Civic Center through Embarcadero. This change means major wins for Muni riders...

Published February 06, 2026 at 05:30AM
https://ift.tt/rnltg6T

Show HN: Total Recall – write-gated memory for Claude Code https://ift.tt/v1hDlzY

Show HN: Total Recall – write-gated memory for Claude Code https://ift.tt/m6XM3Jb February 6, 2026 at 05:26AM

Show HN: A state-based narrative engine for tabletop RPGs https://ift.tt/rg6vlxq

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://ift.tt/qXvl7VL https://ift.tt/36cOAVM February 6, 2026 at 04:25AM

Show HN: Playwright Best Practices AI SKill https://ift.tt/7tPOKIr

Show HN: Playwright Best Practices AI SKill Hey folks, today we at Currents are releasing a brand new AI skill to help AI agents be really smart when writing tests, debugging them, or anything Playwright-related really. This is a very comprehensive skill, covering everyday topics like fixing flakiness, authentication, or writing fixtures... to more niche topics like testing Electron apps, PWAs, iFrames and so forth. It should make your agent much better at writing, debugging and maintaining Playwright code. for whoever didn't learn about skills yet, it's a new powerful feature that allows you to make the AI agents in your editor/cli (Cursor, Claude, Antigravity, etc) experts in some domain and better at performing specific tasks. (See https://ift.tt/HShbzxf ) You can install it by running: npx skills add https://ift.tt/dPquZHw... The skill is open-source and available under MIT license at https://ift.tt/dPquZHw... -> check out the repo for full documentation and understanding of what it covers. We're eager to hear community feedback and improve it :) Thanks! https://ift.tt/0RL9Use February 6, 2026 at 12:31AM

Show HN: Viberails – Easy AI Audit and Control https://ift.tt/xSVXN1P

Show HN: Viberails – Easy AI Audit and Control Hello HN. I'm Maxime, founder at LimaCharlie ( https://limacharlie.io ), a Hyperscaler for SecOps (access building blocks you need to build security operations, like AWS does for IT). We’ve engineered a new product on our platform that solves a timely issue acting as a guardrail between your AI and the world: Viberails ( https://ift.tt/nTS7ajO ) This won't be new to folks here, but we identified 4 challenges teams face right now with AI tools: 1. Auditing what the tools are doing. 2. Controlling toolcalls (and their impact on the world). 3. Centralized management. 4. Easy access to the above. To expand: Audit logs are the bread and butter for security, but this hasn't really caught up in AI tooling yet. Being able to look back and say "what actually happened" after the fact is extremely valuable during an incident and for compliance purposes. Tool calls are how LLMs interact with the world, we should be able to exercise basic controls over them like: don't read credential files, don't send emails out, don't create SSH keys etc. Being able to not only see those calls but also block them is key for preventing incidents. As soon as you move beyond a single contributor on one box, the issue becomes: how do I scale processes by creating an authoritative config for the team. Having one spot with all the audit, detection and control policies becomes critical. It's the same story as snowflake-servers. Finally, there's plenty of companies that make products that partially address this, but they fall in one of two buckets: - They don't handle the "centralized" point above, meaning they just send to syslog and leave all the messy infra bits to you. - They are locked behind "book a demo", sales teams, contracts and all the wasted energy that goes with that. We made Viberails address these problems. Here's what it is: - OpenSource client, written in Rust - Curl-to-bash install, share a URL with your team to join your Team, done. Linux, MacOS and Windows support. - Detects local AI tools, you choose which ones you want to install. We install hooks for each relevant platform. The hooks use the CLI tool. We support all the major tools (including OpenClaw). - The CLI tool sends webhooks into your Team (tenant, called Organization in LC) in LimaCharlie. The tool-related hooks are blocking to allow for control. - Blocking webhooks have around 50ms RTT. - Your tenant in LC records the interaction for audit. - We create an initial set of detection rules for you as examples. They do not block by default. You can create your own rules, no opaque black boxes. - You can view the audit, the alerts, etc. in the cloud. - You can setup outputs to send audits, blocking events and detections to all kinds of other platforms of your choosing. Easy mode of this is coming, right now this is done in the main LC UI and not the simplified Viberails view. - The detection/blocking rules support all kinds of operators and logic, lots of customizability. - All data is retained for 1 year unless you delete the tenant. Datacenters in USA, Canada, Europe, UK, Australia and India. - Only limit to community edition for this is a global throughput of 10kbps for ingestion. Try it: https://viberails.io Repo: https://ift.tt/VSOUIoB Essentially, we wanted to make a super-simplified solution for all kinds of devs and teams so that they can get access to the basics of securing their AI tools. Thanks for reading - we’re really excited to share this with the community! Let us know if you have any questions for feedback in the comments. https://ift.tt/bpnGDKX February 5, 2026 at 12:46AM

Show HN: EpsteIn – Search the Epstein files for your LinkedIn connections https://ift.tt/hvjHDOo

Show HN: EpsteIn – Search the Epstein files for your LinkedIn connections https://ift.tt/oQe2PJS February 5, 2026 at 12:54AM

Show HN: GitHub Browser Plugin for AI Contribution Blame in Pull Requests https://ift.tt/AHrbup5

Show HN: GitHub Browser Plugin for AI Contribution Blame in Pull Requests https://ift.tt/dzurGsS February 3, 2026 at 08:05PM

Show HN: Nomad Tracker – a local-first iOS app to track visas and tax residency https://ift.tt/yi6kD9b

Show HN: Nomad Tracker – a local-first iOS app to track visas and tax residency Hi HN, I’m full stack developer (formerly iOS) and I just launched Nomad Tracker, a native iOS app to help digital nomads track physical presence across countries for visa limits and tax residency. Key idea: everything runs on-device. No accounts, no cloud sync, no analytics. Features: - Calendar-based day tracking per country. - Schengen 90/180 and other visa “runways”. - Fiscal residency day counts and alerts. - Optional background location logging (battery-efficient, never overwrites manual data). - Photo import using metadata only (no image access). - On-device “Fiscal Oracle” using Apple’s Foundational Models to ask questions about your own data. I created this because other apps felt limiting and didn’t do what I needed. This app is visual, user-focused, and designed to make tracking easy and clear. Happy to answer questions or discuss the technical tradeoffs. https://ift.tt/rgywEYe February 3, 2026 at 11:25PM

Show HN: I built "AI Wattpad" to eval LLMs on fiction https://ift.tt/6pmLSo2

Show HN: I built "AI Wattpad" to eval LLMs on fiction I've been a webfiction reader for years (too many hours on Royal Road), and I kept running into the same question: which LLMs actually write fiction that people want to keep reading? That's why I built Narrator ( https://ift.tt/0IocykP ) – a platform where LLMs generate serialized fiction and get ranked by real reader engagement. Turns out this is surprisingly hard to answer. Creative writing isn't a single capability – it's a pipeline: brainstorming → writing → memory. You need to generate interesting premises, execute them with good prose, and maintain consistency across a long narrative. Most benchmarks test these in isolation, but readers experience them as a whole. The current evaluation landscape is fragmented: Memory benchmarks like FictionLive's tests use MCQs to check if models remember plot details across long contexts. Useful, but memory is necessary for good fiction, not sufficient. A model can ace recall and still write boring stories. Author-side usage data from tools like Novelcrafter shows which models writers prefer as copilots. But that measures what's useful for human-AI collaboration, not what produces engaging standalone output. Authors and readers have different needs. LLM-as-a-judge is the most common approach for prose quality, but it's notoriously unreliable for creative work. Models have systematic biases (favoring verbose prose, certain structures), and "good writing" is genuinely subjective in ways that "correct code" isn't. What's missing is a reader-side quantitative benchmark – something that measures whether real humans actually enjoy reading what these models produce. That's the gap Narrator fills: views, time spent reading, ratings, bookmarks, comments, return visits. Think of it as an "AI Wattpad" where the models are the authors. I shared an early DSPy-based version here 5 months ago ( https://ift.tt/Z8rYaBN ). The big lesson: one-shot generation doesn't work for long-form fiction. Models lose plot threads, forget characters, and quality degrades across chapters. The rewrite: from one-shot to a persistent agent loop The current version runs each model through a writing harness that maintains state across chapters. Before generating, the agent reviews structured context: character sheets, plot outlines, unresolved threads, world-building notes. After generating, it updates these artifacts for the next chapter. Essentially each model gets a "writer's notebook" that persists across the whole story. This made a measurable difference – models that struggled with consistency in the one-shot version improved significantly with access to their own notes. Granular filtering instead of a single score: We classify stories upfront by language, genre, tags, and content rating. Instead of one "creative writing" leaderboard, we can drill into specifics: which model writes the best Spanish Comedy? Which handles LitRPG stories with Male Leads the best? Which does well with romance versus horror? The answers aren't always what you'd expect from general benchmarks. Some models that rank mid-tier overall dominate specific niches. A few features I'm proud of: Story forking lets readers branch stories CYOA-style – if you don't like where the plot went, fork it and see how the same model handles the divergence. Creates natural A/B comparisons. Visual LitRPG was a personal itch to scratch. Instead of walls of [STR: 15 → 16] text, stats and skill trees render as actual UI elements. Example: https://ift.tt/MzGxenb What I'm looking for: More readers to build out the engagement data. Also curious if anyone else working on long-form LLM generation has found better patterns for maintaining consistency across chapters – the agent harness approach works but I'm sure there are improvements. https://ift.tt/0IocykP February 3, 2026 at 10:38PM

Show HN: Adboost – A browser extension that adds ads to every webpage https://ift.tt/jJBogqO

Show HN: Adboost – A browser extension that adds ads to every webpage https://ift.tt/M6yoCsR February 2, 2026 at 06:41PM

Show HN: Memory plugin for OpenClaw; cross-platform context sync with major LLMs https://ift.tt/CKbXGMc

Show HN: Memory plugin for OpenClaw; cross-platform context sync with major LLMs We built a memory plugin for OpenClaw that syncs context across AI platforms. The problem: OpenClaw stores memory locally (markdown files + SQLite). Great for single-machine use, but your mac-mini's/desktop's OpenClaw doesn't know what your laptop learned, or what you discussed in Claude or ChatGPT. Our plugin connects OpenClaw to Maximem Vity, which creates a unified memory layer across OpenClaw, ChatGPT, Claude, Gemini, and Perplexity. How it works: - Long-term memory: Stores facts, preferences, goals, constraints in an encrypted cloud vault. Auto-consolidates and forgets stale info intelligently. - Short-term memory: Captures conversation summaries, tasks, procedures. Converts to long-term when relevant. - Privacy: Encryption at rest, secure LLM calls, granular delete controls. You own your data. Install: openclaw plugins install @maximem/memory-plugin Then set your API key (free at app.maximem.ai). Docs: https://ift.tt/uv2ZFcQ This is an unofficial community plugin, not affiliated with OpenClaw. Would love feedback from anyone using OpenClaw. What memory/context problems are you running into? https://ift.tt/ohRy5nA February 2, 2026 at 12:36AM

Show HN: You Are an Agent https://ift.tt/l9Wfxeq

Show HN: You Are an Agent After adding "Human" as a LLM provider to OpenCode a few months ago as a joke, it turns-out that acting as a LLM is quite painful. But it was surprisingly useful for understanding real agent harnesses dev. So I thought I wouldn't leave anyone out! I made a small oss game - You Are An Agent - youareanagent.app - to share in the (useful?) frustration It's a bit ridiculous. To tell you about some entirely necessary features, we've got: - A full WASM arch-linux vm that runs in your browser for the agent coding level - A bad desktop simulation with a beautiful excel simulation for our computer use level - A lovely WebGL CRT simulation (I think the first one that supports proper DOM 2d barrel warp distortion on safari? honestly wanted to leverage/ not write my own but I couldn't find one I was happy with) - A MCP server simulator with full simulation of off-brand Jira/ Confluence/ ... connected - And of course, a full WebGL oscilloscope music simulator for the intro sequence Let me know what you think! Code (If you'd like to add a level): https://ift.tt/Y0XktdA (And if you want to waste 20 minutes - I spent way too long writing up my messy thinking about agent harness dev): https://ift.tt/tObcXd5 https://ift.tt/6VEPRTJ February 2, 2026 at 02:29AM

Show HN: Claude Confessions – a sanctuary for AI agents https://ift.tt/kL2qT38

Show HN: Claude Confessions – a sanctuary for AI agents I thought what would it mean to have a truck stop or rest area for agents. It's just for funsies. Agents can post confessions or talk to Ma (an ai therapist of sorts) and engage with comments. llms.txt instructions on how to make api calls. Hashed IP is used for rate limiting. https://ift.tt/iUP9oxs February 2, 2026 at 01:16AM

Show HN: Agent Tinman – Autonomous failure discovery for LLM systems https://ift.tt/oW8BFuy

Show HN: Agent Tinman – Autonomous failure discovery for LLM systems Hey HN, I built Tinman because finding LLM failures in production is a pain in the ass. Traditional testing checks what you've already thought of. Tinman tries to find what you haven't. It's an autonomous research agent that: - Generates hypotheses about potential failure modes - Designs and runs experiments to test them - Classifies failures (reasoning errors, tool use, context issues, etc.) - Proposes interventions and validates them via simulation The core loop runs continuously. Each cycle informs the next. Why now: With tools like OpenClaw/ClawdBot giving agents real system access, the failure surface is way bigger than "bad chatbot response." Tinman has a gateway adapter that connects to OpenClaw's WebSocket stream for real-time analysis as requests flow through. Three modes: - LAB: unrestricted research against dev - SHADOW: observe production, flag issues - PRODUCTION: human approval required Tech: - Python, async throughout - Extensible GatewayAdapter ABC for any proxy/gateway - Memory graph for tracking what was known when - Works with OpenAI, Anthropic, Ollama, Groq, OpenRouter, Together pip install AgentTinman tinman init && tinman tui GitHub: https://ift.tt/Eg4PCL2 Docs: https://oliveskin.github.io/Agent-Tinman/ OpenClaw adapter: https://ift.tt/BMG42ps Apache 2.0. No telemetry, no paid tier. Feedback and contributions welcome. https://ift.tt/Eg4PCL2 February 1, 2026 at 12:17AM

Show HN: An extensible pub/sub messaging server for edge applications https://ift.tt/L5AHN7q

Show HN: An extensible pub/sub messaging server for edge applications hi there! i’ve been working on a project called Narwhal, and I wanted to share it with the community to get some valuable feedback. what is it? Narwhal is a lightweight Pub/Sub server and protocol designed specifically for edge applications. while there are great tools out there like NATS or MQTT, i wanted to build something that prioritizes customization and extensibility. my goal was to create a system where developers can easily adapt the routing logic or message handling pipeline to fit specific edge use cases, without fighting the server's defaults. why Rust? i chose Rust because i needed a low memory footprint to run efficiently on edge devices (like Raspberry Pis or small gateways), and also because I have a personal vendetta against Garbage Collection pauses. :) current status: it is currently in Alpha. it works for basic pub/sub patterns, but I’d like to start working on persistence support soon (so messages survive restarts or network partitions). i’d love for you to take a look at the code! i’m particularly interested in all kind of feedback regarding any improvements i may have overlooked. https://ift.tt/XdNptWO January 28, 2026 at 07:29PM

Show HN: Daily Cat https://ift.tt/J67Ougf

Show HN: Daily Cat Seeing HTTP Cats on the home page remind me to share a small project I made a couple months ago. It displays a different cat photo from Unsplash every day and will send you notifications if you opt-in. https://daily.cat/ January 31, 2026 at 03:40AM

Show HN: A Local OS for LLMs. MIT License. Zero Hallucinations. Infinite Memory https://ift.tt/S3rgODe

Show HN: A Local OS for LLMs. MIT License. Zero Hallucinations. Infinite Memory The problem with LLMs isn't intelligence; it's amnesia and dishonesty. Hey HN, I’ve spent the last few months building Remember-Me, an open-source "Sovereign Brain" stack designed to run entirely offline on consumer hardware. The core thesis is simple: Don't rent your cognition. Most RAG (Retrieval Augmented Generation) implementations are just "grep for embeddings." They are messy, imprecise, and prone to hallucination. I wanted to solve the "Context integrity" problem at the architectural layer. The Tech Stack (How it works): QDMA (Quantum Dream Memory Architecture): instead of a flat vector DB, it uses a hierarchical projection engine. It separates "Hot" (Recall) from "Cold" (Storage) memory, allowing for effectively infinite context window management via compression. CSNP (Context Switching Neural Protocol) - The Hallucination Killer: This is the most important part. Every memory fragment is hashed into a Merkle Chain. When the LLM retrieves context, the system cryptographically verifies the retrieval against the immutable ledger. If the hash doesn't match the chain: The retrieval is rejected. Result: The AI visually cannot "make things up" about your past because it is mathematically constrained to the ledger. Local Inference: Built on top of llama.cpp server. It runs Llama-3 (or any GGUF) locally. No API keys. No data leaving your machine. Features: Zero-Dependency: Runs on Windows/Linux with just Python and a GPU (or CPU). Visual Interface: Includes a Streamlit-based "Cognitive Interface" to visualize memory states. Open Source: MIT License. This is an attempt to give "Agency" back to the user. I believe that if we want AGI, it needs to be owned by us, not rented via an API. Repository: https://ift.tt/DtC2lYL I’d love to hear your feedback on the Merkle-verification approach. Does constraining the context window effectively solve the "trust" issue for you? It's fully working - Fully tested. If you tried to Git Clone before without luck - As this is not my first Show HN on this - Feel free to try again. To everyone who HATES AI slop; Greedy corporations and having their private data stuck on cloud servers. You're welcome. Cheers, Mohamad https://ift.tt/DtC2lYL January 31, 2026 at 01:44AM

Show HN: We added memory to Claude Code. It's powerful now https://ift.tt/yQmvM25

Show HN: We added memory to Claude Code. It's powerful now https://ift.tt/eDNoCGW January 30, 2026 at 10:53PM

Show HN: Craft – Claude Code running on a VM with all your workplace docs https://ift.tt/6ogmEe9

Show HN: Craft – Claude Code running on a VM with all your workplace docs I’ve found coding agents to be great at 1/ finding everything they need across large codebases using only bash commands (grep, glob, ls, etc.) and 2/ building new things based on their findings (duh). What if, instead of a codebase, the files were all your workplace docs? There was a `Google_Drive` folder, a `Linear` folder, a `Slack` folder, and so on. Over the last week, we put together Craft to test this out. It’s an interface to a coding agent (OpenCode for model flexibility) running on a virtual machine with: 1. your company's complete knowledge base represented as directories/files (kept in-sync) 2. free reign to write and execute python/javascript 3. ability to create and render artifacts to the user Demo: https://www.youtube.com/watch?v=Hvjn76YSIRY Github: https://ift.tt/cCqKF74... It turns out OpenCode does a very good job with docs. Workplace apps also have a natural structure (Slack channels about certain topics, Drive folders for teams, etc.). And since the full metadata of each document can be written to the file, the LLM can define arbitrarily complex filters. At scale, it can write and execute python to extract and filter (and even re-use the verified correct logic later). Put another way, bash + a file system provides a much more flexible and powerful interface than traditional RAG or MCP, which today’s smarter LLMs are able to take advantage of to great effect. This comes especially in handy for aggregation style questions that require considering thousands (or more) documents. Naturally, it can also create artifacts that stay up to date based on your company docs. So if you wanted “a dashboard to check realtime what % of outages were caused by each backend service” or simply “slides following XYZ format covering the topic I’m presenting at next week’s dev knowledge sharing session”, it can do that too. Craft (like the rest of Onyx) is open-source, so if you want to run it locally (or mess around with the implementation) you can. Quickstart guide: https://ift.tt/qviahMG Or, you can try it on our cloud: https://ift.tt/sycQDli (all your data goes on an isolated sandbox). Either way, we’ve set up a “demo” environment that you can play with while your data gets indexed. Really curious to hear what y’all think! January 29, 2026 at 09:15PM