The best Hacker News stories from Show from the past day
Latest posts:
Show HN: Amaze Kings – a browser logic puzzle with 1,400 hand-graded levels
Show HN: Getting to the top of Show HN is getting harder
Show HN: Personal Context MCP
Show HN: Moadim.io – A scheduler for agents
Why can't we get an agent scheduler that supports all of the following:<p>- git compatible<p>- agent agnostic<p>- 100% open source<p>- os and system-agnostic<p>- multi-runner support<p>- support mcp/ui/http<p>- unlimited routines/crons<p>So I built one, moadim.io is a local Rust daemon you install in the target machine, give it a name and manage its routines via a Git repository, wants a new routine that send you a daily message from this machine? Create a pr and merge, have another routine that run every hour to pull the latest changes to the ~/.config/moadim folder.<p>With more than 1,000 users, I define this project as almost "done" and ready for production. Me and thousand more people are use it in a daily manner.<p>It currently supports Claude, Codex, Hermes, and Pi, and you are welcome to add your agent of choice as well because it's 100% configurable.<p>You are welcome to have a look at the source code of the daemon in "<a href="http://github.com/moadim-io/daemon" rel="nofollow">http://github.com/moadim-io/daemon</a>"<p>Feel free to provide me with suggestions for more features around this topic. I don't want to branch out to new off road topics likt webhooks, this is a "done" software in the realm of agents schedulers that focus on cron-like work.<p>Also feel free to start the github repository and open issues and PRs for your suggestions.
Show HN: Fly By – retro biplane flying game
Feedback welcome. Best on desktop. Works on mobile, but needs more polish there.<p>My concept, my guidance, and Gemini Flash doing the work. Took about 4 hours and 50+ conversations with Gemini.<p>Big Kudos to Gemini Flash (since 3.5 and later), because I have had great success with it on many projects.
Show HN: Prices.eu.org
When Ox-alpha was free I had it build this EU price index/tracker (the build took 40+ hours).<p>Gemini deep research found the sources.<p>Currently it tracks:<p>- Fuel<p>- Electricity<p>- Currency<p>- Housing<p>- Groceries<p>- Wages<p>- Purchasing power
Show HN: Reactor Atlas
Hi HN, I'm a nuclear engineer and tech entrepreneur. After working for 3 years in the National Atomic Energy Commision (CNEA) in HPC software for nuclear simulations, I co-founded ratherlabs.com, a software company providing blockchain solutions for startups. Rather Labs business reached ~6 million usd in annual revenue in 2024; now demand is decreasing as our niche clients are using AI to launch their MVPs.<p>Now I'm joining my scientific background with my entrepreneurial inclinations to think of problems related to the energy industry. Nuclear is experiencing a renaissance now (it was in a dark age when I was studying after the Fukushima event) so I think this is the right moment for me to jump again into the field.<p>Reactor atlas is an interactive nuclear reactor map and an intelligence layer on top of it. The basic layer indexes:<p><pre><code> - power reactors
- research reactors
- fuel facilities
</code></pre>
with their history and projection, by country. The project is solving a fragmented information ecosystem, where operational and commercial data is hard to conciliate. Also, I'm monitoring nuclear news associated with each of the facilities, political declarations that may affect the nuclear landscape and geo-phisical news (like earthquakes) that could affect operation. On top of the operational + news layer, I'm creating an intelligence layer that will help institutions of different types make faster decisions by subscribing to very specific alerts.<p>As an engineer, I used to code in fortran and c++ when I was working at CNEA, and later I participated in the first Rather Labs projects architecting complex async systems, mostly using MERN stack. I'm no longer coding anymore. I'm focused on designing robust systems with AI agents.<p>I mainly used Claude Fable 5.1 to develop reactoratlas.com, deploying on vercel and using mostly nextjs, threejs, postgreSQL and some cron jobs to query and update data. I miss coding as in the old days, but I haven't code a single line in this project. And I strongly believe we humans shouldn't code anymore: our responsibility is now on designing and supervising AI systems.<p>Thanks for your attention!
Show HN: Reactor Atlas
Hi HN, I'm a nuclear engineer and tech entrepreneur. After working for 3 years in the National Atomic Energy Commision (CNEA) in HPC software for nuclear simulations, I co-founded ratherlabs.com, a software company providing blockchain solutions for startups. Rather Labs business reached ~6 million usd in annual revenue in 2024; now demand is decreasing as our niche clients are using AI to launch their MVPs.<p>Now I'm joining my scientific background with my entrepreneurial inclinations to think of problems related to the energy industry. Nuclear is experiencing a renaissance now (it was in a dark age when I was studying after the Fukushima event) so I think this is the right moment for me to jump again into the field.<p>Reactor atlas is an interactive nuclear reactor map and an intelligence layer on top of it. The basic layer indexes:<p><pre><code> - power reactors
- research reactors
- fuel facilities
</code></pre>
with their history and projection, by country. The project is solving a fragmented information ecosystem, where operational and commercial data is hard to conciliate. Also, I'm monitoring nuclear news associated with each of the facilities, political declarations that may affect the nuclear landscape and geo-phisical news (like earthquakes) that could affect operation. On top of the operational + news layer, I'm creating an intelligence layer that will help institutions of different types make faster decisions by subscribing to very specific alerts.<p>As an engineer, I used to code in fortran and c++ when I was working at CNEA, and later I participated in the first Rather Labs projects architecting complex async systems, mostly using MERN stack. I'm no longer coding anymore. I'm focused on designing robust systems with AI agents.<p>I mainly used Claude Fable 5.1 to develop reactoratlas.com, deploying on vercel and using mostly nextjs, threejs, postgreSQL and some cron jobs to query and update data. I miss coding as in the old days, but I haven't code a single line in this project. And I strongly believe we humans shouldn't code anymore: our responsibility is now on designing and supervising AI systems.<p>Thanks for your attention!
Show HN: TERMy – A fast terminal assistant that does not use LLMs
I love research and development, you may have heard of me because of PJON (Padded Jittering Operative Network). It is a network protocol I started developing in 2010, which was recently implemented in silicon by the ETH Zurich university thanks to the research of Pius Sieber.<p>I am excited to share with you TERMy, a terminal assistant built on top of the NPC-Forge framework. Unlike everything else being built today, TERMy does not use embeddings, machine-learning or LLMs. It runs on the CPU (even on a Raspberry Pi Zero) both in the terminal or client-side in a browser tab and responds in milliseconds. It is a cynical but very knowledgeable Linux terminal assistant that translates your natural language into shell commands without relying on a single artificial neuron.<p>I had a chance to focus for 2 months on my personal projects since early July, during the strange times of AI price hikes and the end of subsidized tokenmaxing. I was curious to see if I could develop from scratch a terminal assistant capable of handling simple natural language requests. I have a bad memory and got used to ask to copilot "activate the virtual environment" or similar trivial operations spending a non negligible sum every month. I started thinking, maybe I can do something to make my workflow more efficient? Do I really need trillions of parameters to accomplish those tasks?<p>How it Works<p>When you type a prompt, it goes through a lightweight NLU pipeline written in ~1000 lines of Python that implement the following steps:<p>1. Strip expletives, interjections, encouraging, discouraging and thanking words (remove noise)<p>2. Sentiment analysis<p>3. Exact Match (very fast)<p>4. Template Match (slower)<p>5. Probabilistic Match (even slower)<p>Step 5 relies on:<p>1. IDF (Inverse Document Frequency) to identify rare words.<p>2. BOW (Bag Of Words) to accommodate word inversions.<p>3. IDF weighted Levenshtein to safely handle typos.<p>Permission gating is hardcoded into the dataset and enforced for all potentially destructive commands, so it's inherently safer than letting an unpredictable LLM run wild on your machine.<p>- TERMy in operation: <a href="https://www.youtube.com/watch?v=qeIp0xePLBg" rel="nofollow">https://www.youtube.com/watch?v=qeIp0xePLBg</a><p>- Variance and typo tolerance: <a href="https://www.youtube.com/watch?v=tQvGDk6fkk0" rel="nofollow">https://www.youtube.com/watch?v=tQvGDk6fkk0</a><p>- Copilot integration: <a href="https://www.youtube.com/watch?v=Wzzouhq2a8A" rel="nofollow">https://www.youtube.com/watch?v=Wzzouhq2a8A</a><p>- Advanced features: <a href="https://www.youtube.com/watch?v=qeIp0xePLBg" rel="nofollow">https://www.youtube.com/watch?v=qeIp0xePLBg</a><p>- Source Code: <a href="https://github.com/gioblu/NPC-Forge" rel="nofollow">https://github.com/gioblu/NPC-Forge</a>
Show HN: TERMy – A fast terminal assistant that does not use LLMs
I love research and development, you may have heard of me because of PJON (Padded Jittering Operative Network). It is a network protocol I started developing in 2010, which was recently implemented in silicon by the ETH Zurich university thanks to the research of Pius Sieber.<p>I am excited to share with you TERMy, a terminal assistant built on top of the NPC-Forge framework. Unlike everything else being built today, TERMy does not use embeddings, machine-learning or LLMs. It runs on the CPU (even on a Raspberry Pi Zero) both in the terminal or client-side in a browser tab and responds in milliseconds. It is a cynical but very knowledgeable Linux terminal assistant that translates your natural language into shell commands without relying on a single artificial neuron.<p>I had a chance to focus for 2 months on my personal projects since early July, during the strange times of AI price hikes and the end of subsidized tokenmaxing. I was curious to see if I could develop from scratch a terminal assistant capable of handling simple natural language requests. I have a bad memory and got used to ask to copilot "activate the virtual environment" or similar trivial operations spending a non negligible sum every month. I started thinking, maybe I can do something to make my workflow more efficient? Do I really need trillions of parameters to accomplish those tasks?<p>How it Works<p>When you type a prompt, it goes through a lightweight NLU pipeline written in ~1000 lines of Python that implement the following steps:<p>1. Strip expletives, interjections, encouraging, discouraging and thanking words (remove noise)<p>2. Sentiment analysis<p>3. Exact Match (very fast)<p>4. Template Match (slower)<p>5. Probabilistic Match (even slower)<p>Step 5 relies on:<p>1. IDF (Inverse Document Frequency) to identify rare words.<p>2. BOW (Bag Of Words) to accommodate word inversions.<p>3. IDF weighted Levenshtein to safely handle typos.<p>Permission gating is hardcoded into the dataset and enforced for all potentially destructive commands, so it's inherently safer than letting an unpredictable LLM run wild on your machine.<p>- TERMy in operation: <a href="https://www.youtube.com/watch?v=qeIp0xePLBg" rel="nofollow">https://www.youtube.com/watch?v=qeIp0xePLBg</a><p>- Variance and typo tolerance: <a href="https://www.youtube.com/watch?v=tQvGDk6fkk0" rel="nofollow">https://www.youtube.com/watch?v=tQvGDk6fkk0</a><p>- Copilot integration: <a href="https://www.youtube.com/watch?v=Wzzouhq2a8A" rel="nofollow">https://www.youtube.com/watch?v=Wzzouhq2a8A</a><p>- Advanced features: <a href="https://www.youtube.com/watch?v=qeIp0xePLBg" rel="nofollow">https://www.youtube.com/watch?v=qeIp0xePLBg</a><p>- Source Code: <a href="https://github.com/gioblu/NPC-Forge" rel="nofollow">https://github.com/gioblu/NPC-Forge</a>
Show HN: Open-Source eInk Bike Computer
Hey all, i just launched my Eink Bike computer project and think it is cool.<p>Another tidbit, in the crazy things that AI has done... It has helped create a ANT (common sensor wireless protocol used in workout/biking) implementation for ESP32 by messing around with undocumented registers: <a href="https://github.com/RaemondBW/esp32-ant" rel="nofollow">https://github.com/RaemondBW/esp32-ant</a>
Show HN: Open-Source eInk Bike Computer
Hey all, i just launched my Eink Bike computer project and think it is cool.<p>Another tidbit, in the crazy things that AI has done... It has helped create a ANT (common sensor wireless protocol used in workout/biking) implementation for ESP32 by messing around with undocumented registers: <a href="https://github.com/RaemondBW/esp32-ant" rel="nofollow">https://github.com/RaemondBW/esp32-ant</a>
Show HN: asciiQuake
Show HN: Real-time AI news aggregator with daily digest
Show HN: Real-time AI news aggregator with daily digest
Show HN: I built my first MCP to manage Google Ads
My co-founder and I tried running Google Ads for our Shopify store, but we could not get a positive ROAS (we spent 150USD on a single conversion).<p>As we also could not afford an agency, we turned to AI (IMO Codex >>> Claude Code).<p>We found that Google’s Ads MCP could only read data, but it could not make changes. So we built our own hosted MCP with read and write access.<p>It can inspect performance, find wasted spend, create or update campaigns, and work with Google Analytics and Tag Manager.
Show HN: I built my first MCP to manage Google Ads
My co-founder and I tried running Google Ads for our Shopify store, but we could not get a positive ROAS (we spent 150USD on a single conversion).<p>As we also could not afford an agency, we turned to AI (IMO Codex >>> Claude Code).<p>We found that Google’s Ads MCP could only read data, but it could not make changes. So we built our own hosted MCP with read and write access.<p>It can inspect performance, find wasted spend, create or update campaigns, and work with Google Analytics and Tag Manager.
Show HN: The cheapest GPU cloud – H100s at $2.04/HR, H200s at $3/HR
Show HN: The cheapest GPU cloud – H100s at $2.04/HR, H200s at $3/HR
Show HN: Aura – a Rust agent that investigates and fixes production incidents
We run a SaaS that handles petabytes of data. Our SRE team experimented with using claude, openclaw, langchain, etc. within our incident response workflows. We struggled with overflowing context, lethal trifecta vectors, hallucinations, and burned a lot of frontier tokens mostly on easy work. Approval fatigue was a challenge, and we drew a hard line at relaxing permissions in production.<p>Long story short, we built and open-sourced AURA, a Rust-based harness specifically designed for the type of operations work which routinely involves large volumes of telemetry data and coordinated investigations across many domains of knowledge / state. We have found even on open-weights models, root cause accuracy has been very good, and remediation actions are guarded with human-in-the-loop.<p>AURA runs from a centralized configuration file where workers are defined and scoped to task domains, (e.g. logging review, metrics analysis, and git/scm related queries). All permissions, tools access, LLM backend(s), worker prompts, and the main coordinator prompt are also defined in code. Permitted tool use is enforced deterministically outside the agent's context, so the agent cannot grant itself capabilities through prompting. AURA also handles the rest of the execution layer, such as human approval for sensitive actions and fault tolerance. A lot of thought went into context window management: large tool outputs and worker responses are persisted to disk and agents are given tools to slice/read as needed.<p>The project is Apache 2.0. You can install rpm/deb/brew natively or pull the source and build your own. It runs either as an assistant on your workstation, or as a daemon that you can connect to via any OpenAI-compatible frontend. The GitHub repo is here: <a href="https://github.com/mezmo/aura/" rel="nofollow">https://github.com/mezmo/aura/</a><p>The whole thing is free/Free. No signups or open core trapdoor.<p>Demo: We put together an 8-minute video showing a checkout outage resolved by following evidence from several different systems: <a href="https://www.youtube.com/watch?v=TERHoRzT8cE" rel="nofollow">https://www.youtube.com/watch?v=TERHoRzT8cE</a><p>tl;dw: 502s from a checkout service cause Grafana to trigger an incident via PagerDuty. AURA (using DeepSeek-V4-Flash) uses its workers and tools to correctly determine root cause as a memory leak in a downstream service, and attributes the incident to a defect in a recently merged PR. The demo then concludes with a human-gated tool call to GitHub, where AURA documents the exact lines of code that are causing the problem, and recommends a fix. Once the PR is deployed, AURA validates that the errors are clearing and transactions are no longer failing. The whole time, AURA’s leaving an audit trail of OTEL events into Phoenix.<p>Technical details:<p>AURA uses an agent coordinator that drafts, executes, and supervises DAG flows through user-defined worker agents to solve complex investigations in parallel. Worker agents are bound to strongly typed durable artifacts used as evidence packets, and the system discourages wasteful recalculation of remote data. AURA also has a powerful header routing system that doesn’t allow the agent to actually touch your keys. HITL interrupts over webhook are first class citizens and have a schema that is easy to adapt to your own workflow. HA deployment options are coming soon.<p>What’s still rocky:<p>- We’re currently working on the async input system for when you’re running AURA as a service. Right now the API just accepts a request, and streams messages until the main loop completes. It’s easy to integrate into a workflow, but the logic required winds up being heavier than it ought to be.<p>- Because there’s no inbound webhook interrupt mechanism, automating AURA for IR requires middleware to invoke it or it needs to poll an MCP for alert escalations.<p>We’re looking for users, contributors, guidance on where to take this next, or just GitHub stars. Looking forward to reading and responding to feedback here.