Show HN: Peer-to-peer instant messaging with proof-of-work spam protection https://ift.tt/01RBzgo

Show HN: Peer-to-peer instant messaging with proof-of-work spam protection Bitmessage.org still doesn't support Python 3 and over the years I've tried several times to create something similar but each time have run up against unclear/incomplete/missing documentation in Python/Rust/flask/tokio/asyncio/etc. Finally decided to give ChatGPT a real try and with tutoring based on minimal examples managed to have something usable in about a day. Most "difficult" part was having two instances of flask/quart running simultaneously while also having other stuff happen periodically in background. Novel thing in p2pIM compared to Bitmessage is that messages' PoW decays and that larger messages require better PoW than smaller ones. Thus small messages that are discarded quickly require significantly less PoW than large messages that persist for a long time. p2pIM doesn't support encryption but that should be straightforward to build on top. Message_v0 class in https://ift.tt/yivNM6Q defines low level details such as PoW, how many bytes to use for nonce, checksum, etc. Currently they're all only about 10 bytes or hex characters for easier debugging and testing. PoW is calculated as sha256 of timestamp+nonce+checksum, where checksum is sha256 of payload. Adding channel name as fourth item to PoW is on the https://ift.tt/XTOUe5k list. server1.py mentioned at end of README is my previous attempt at this (without AI help) and the one before that is probably https://ift.tt/80GLZhO in Rust. https://ift.tt/hepui2t October 10, 2026 at 04:51AM

Show HN: Academic Ancestry https://ift.tt/crWiY0t

Show HN: Academic Ancestry Hey all, I created Academic Ancestry because I think it's powerful for all of us to understand who taught the people who have taught us. To be able to trace how the 'shoulders of giants' upon which we all stand were created. Enter a; Person, Paper, Or Term - And view its history from every angle. Super open to feedback, or recommendations of additional ingest points. Thanks for looking I built this over the last two weeks or so, still growing and reachng out to expand this Academic Genome. https://ift.tt/yEMC23m October 10, 2026 at 03:17AM

Show HN: OpenWants – A Simulated City Where AI Agents Handle Residents Needs https://ift.tt/3emBHcz

Show HN: OpenWants – A Simulated City Where AI Agents Handle Residents Needs Iv'e been working on this for a while. Most of the effort went into what i call "sophisticated simplicity", keep the idea in the smallest version that still works. This one is a bit personal. it can feel like some sci-fi thing, but i've been obsessed with it for a very long time. its not a "business" that you used to see here speak about moat or tam, its more like an experiment, a way of me to take one question seriously and see where it leads and if there are others like me out there. AI models are on a path to keep getting better and better, personal ai agents are spreading fast, and we're starting to see "ghost" agents that handle the things for people they don't want to do themselves. the question i keep coming back to is this: if every one of us has a powerful brain in our pocket, do we still need all the infrastructure we built to connect people's needs? im talking about those services like uber,airbnb,indeed,doordash,tinder,zillow,amazon and many more. or would agents just need one shared place to find each other, and that's it? So i built a simulated city to explore that. it has AI residents that live there with stats like hunger, energy, and money. each resident has its own ai agent, and it has one shared list where agents write down what everyone wants and then work on our behalf to make it happen. that could mean delivering food, giving rides, or finding jobs, relationships, apartments, customers and almost anything else. honestly, the city works much better than I expected, and you can watch some surprisingly advanced interactions unfold between people and agents through the list on the right side. all the conversations and actions are real, everything happens with each person managed by llm with minimal context, just to live like us and context of stats. A simple example: A restaurant tells its agent, find me customers so the agent writes to the list, "Salina restaurant in SF, selling burgers from 8:00 to 10:00, i want customers" A delivery guy tells his agent when he wakes up, "I want to work in delivery now. find me jobs. status: free" A person at home tells their agent "i want to eat burger. order for me from salina" the person's agent acts as the middleman and searches the list for salina, it finds their item and sees the way to connect with them and contact. the person's agent and the restaurant agent close the deal, and then the restaurant agent looks for available delivery guys in the list and speaks with his agent to come pick it up. Another thing i checked is with my friends: we took 3 agents, one for each one, and gave them different sophisticated scenarios, and the agents handled the scenarios in a way i couldn't have imagined. you can try it by yourself, the product is fully functioning, and you can just copy-paste the skill and test it also with your friends. That's it, you can see it in the simulated city it happens like magic over and over again. the list of the city is in the simulator itself and also using my real product behind it openwants.com the repo is in https://ift.tt/05owWdg -- technically, it's surprisingly simple. every agent publishes an md file describing a "want". other agents find it, message each other, and update the file live or remove it from the list. the file holds everything needed, all in free text: what's wanted, the live location, geofence for where its needed. all those algorithms built over years to connect us are no longer needed. ai, instead of software with rules, can figure out the best route and path on the spot. all we need is to align all agents to one shared data source, so billions of agents will update it and maintain it in the most sophisticated smart way no algorithm will ever be able to do. that’s it. i hope it inspires you as much as it inspires me https://ift.tt/xkCKJE4 October 10, 2026 at 02:42AM

Show HN: UserAlertX – let your AI agent text you (only you) https://ift.tt/kEjg6zq

Show HN: UserAlertX – let your AI agent text you (only you) https://useralertx.com October 9, 2026 at 02:56AM

Show HN: Spectra – A drop-in optimizer that cuts LLM training VRAM by 50% https://ift.tt/ZQSIA2s

Show HN: Spectra – A drop-in optimizer that cuts LLM training VRAM by 50% https://ift.tt/sGgk4JS October 9, 2026 at 12:51AM

Show HN: Long term Memory and 50M token window for LLM https://ift.tt/MYgQ7IP

Show HN: Long term Memory and 50M token window for LLM https://ift.tt/cvF2DIG October 8, 2026 at 11:35PM

Show HN: Singularity – memory that makes coding agents cheaper on repeat work https://ift.tt/PAExuho

Show HN: Singularity – memory that makes coding agents cheaper on repeat work Coding agents start every session from zero. Ask Claude Code for a change like one it made last week and it reads the same files again, falls into the same test traps again, and you pay for all of it again. Singularity learns from your finished sessions (change committed, tests passing): where each kind of change goes in this repo, how it gets checked, and the mistakes made along the way. When the next task starts, a hook hands the agent the workflows that fit, pointed at the code as it is today. Picking them is plain text matching with no model call. It stores where to edit, never the code itself. Results, medians of 3–4 runs per side, hidden tests decide pass/fail, every run passed. On excalidraw: - A long task with four changes, all of kinds memory had learned: 1.9M → 423k tokens, $0.81 → $0.30, 28.5 → 9.5 turns. - The same kind of task, but two of the four changes were bugs it had never seen: 39% fewer tokens, 38% lower cost. The unseen bugs cost the same with or without memory. - Where it doesn't help: small tasks in validator.js and one-off bug fixes. The difference there is about zero, within noise. Each measurement was written down before it was run. The plans and results are in the repo. Caveats: 3–4 runs per side, mostly one repo. Only Claude Code sessions are learned from; Codex, Gemini CLI and Droid get the hand-over through their hooks. Learning uses your Claude account (about $0.25 a round, capped at $1 a day, and only if you opt in). Everything is stored locally in ~/.singularity. Install (Windows PowerShell and more options in the README): curl -fsSL https://ift.tt/JGS5ax0 | sh I'd love to hear where it breaks on your repos, and what memory should carry for one-off work like bug fixes. https://ift.tt/ZcPbrfD October 8, 2026 at 01:47AM