Show HN: Local Mitsubishi AC Control with ESPHome and an AtomS3 Lite https://ift.tt/QmDHEbP

Show HN: Local Mitsubishi AC Control with ESPHome and an AtomS3 Lite I connected my Mitsubishi split air conditioners directly to Home Assistant over CN105 using an AtomS3 Lite and ESPHome, avoiding MELCloud entirely. https://ift.tt/gWArv1z September 5, 2026 at 12:30AM

Show HN: TERMy – A fast terminal assistant that does not use LLMs https://ift.tt/dgeyGjs

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. 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. 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? How it Works When you type a prompt, it goes through a lightweight NLU pipeline written in ~1000 lines of Python that implement the following steps: 1. Strip expletives, interjections, encouraging, discouraging and thanking words (remove noise) 2. Sentiment analysis 3. Exact Match (very fast) 4. Template Match (slower) 5. Probabilistic Match (even slower) Step 5 relies on: 1. IDF (Inverse Document Frequency) to identify rare words. 2. BOW (Bag Of Words) to accommodate word inversions. 3. IDF weighted Levenshtein to safely handle typos. 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. - TERMy in operation: https://www.youtube.com/watch?v=qeIp0xePLBg - Variance and typo tolerance: https://www.youtube.com/watch?v=tQvGDk6fkk0 - Copilot integration: https://www.youtube.com/watch?v=Wzzouhq2a8A - Advanced features: https://www.youtube.com/watch?v=qeIp0xePLBg - Source Code: https://ift.tt/APTj98N https://ift.tt/sFZx20y September 4, 2026 at 04:03PM

Show HN: Radia – agent coordination with leases, permissions and provenance https://ift.tt/g6Qq9VO

Show HN: Radia – agent coordination with leases, permissions and provenance https://radia.sh/ September 3, 2026 at 11:59PM

Show HN: I built an app that makes your goals inevitable https://ift.tt/pbrAOja

Show HN: I built an app that makes your goals inevitable https://ift.tt/wN0KlAV September 3, 2026 at 11:55PM

Show HN: A Context Registry for AI coding agents https://ift.tt/YM3Rw6D

Show HN: A Context Registry for AI coding agents Hi HN. We built an API context registry to help coding agents (like Claude Code) generate production-ready API integration code without blowing through token limits. We build a lot of API integrations. In our experience, most coding agents write basic client calls fine, but consistently stumble on details that make code shippable, like idempotent retries, rate-limiting and Auth token management. We tried all the existing approaches of injecting context into coding sessions: - Markdown dumps delivered via MCP (think Context7 or Mintlify Docs MCP) - API behaviour described in prose using AGENTS.md and skills. - OpenAPI specs However, all of them left the same production-readiness gaps. So we came up with our own approach that combines prose with typed SDK reference code into a "Context Plugin". You install the plugin into your coding agent and it automatically injects language-specific context whenever the agent works on an API. Across our benchmarks, Context Plugins boosted one-shot production readiness by up to 34%, allowing Sonnet to match or beat baseline Opus on the same integration tasks. You can read more about our experiments here https://ift.tt/YNewklD... We have published Context Plugins for 24 APIs for the community to try out, including Slack, Google Maps, and Notion. We'd love for you to give them a go and share your feedback on our plugins as well as our evaluation methodology. https://ift.tt/PrA5nMG September 3, 2026 at 11:00PM

Show HN: Kekoso – On-device dictation and transcription for macOS https://ift.tt/pxkwd9l

Show HN: Kekoso – On-device dictation and transcription for macOS https://kekoso.app/ September 2, 2026 at 11:51PM

Show HN: Skatanica – Browse and share skate clips by location https://ift.tt/0kud52K

Show HN: Skatanica – Browse and share skate clips by location https://skatanica.com (no login or download required) I love Dern Bros skate spot history videos, and wanted to see 1.) places my favorite skaters have skated 2.) which skaters had skated my favorite places 3.) find new skaters and new places 4.) have an easy way to just watch clips 5.) have an easy place to document tricks I/people have done anywhere Some examples: 1.) Clips from Dominick Walker: https://ift.tt/reO4UIs 2.) Skaters at Santa Monica Triple Set: https://ift.tt/2PF81A5 3.) Find some spots/clips from spots in [Southern California]( https://ift.tt/nCHAPBt ) , or [Southeast Asia]( https://ift.tt/I6hXloS ) --- Contributing as a Curator: If you like this, and you want to go beyond just sharing a clip here or there, I've built a ton of curator tools specifically for quickly chopping up long videos into clips and labeling the tricks/publishing directly to platform. Message me or comment and I can give you more info/get you set up. https://ift.tt/ceu64CD September 2, 2026 at 11:33PM

Show HN: Aura – a Rust agent that investigates and fixes production incidents https://ift.tt/kZIjG7C

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. 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. 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. 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: https://ift.tt/bX8Ex4U The whole thing is free/Free. No signups or open core trapdoor. Demo: We put together an 8-minute video showing a checkout outage resolved by following evidence from several different systems: https://www.youtube.com/watch?v=TERHoRzT8cE 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. Technical details: 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. What’s still rocky: - 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. - 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. 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. https://ift.tt/NjDuxqB September 2, 2026 at 10:55PM

Show HN: Newton's Orchard – Browser-based space/gravity playground https://ift.tt/8TzdN3v

Show HN: Newton's Orchard – Browser-based space/gravity playground Hi HN! My son spent a lot of time in 5th grade playing with the 2-dimensional PhET gravity/space sims[1]. His STEM class uses it, and he was playing with it at home, and has been looking for more. I found several space sims/playgrounds online, including NASA's Eyes on the Solar System[2], but nothing that seemed to offer a real progression from what he was getting out of the PhET sims, so I built Newton's Orchard over the summer for him. https://ift.tt/rglLDpx The source is also published at https://ift.tt/81gsXuB It features deterministic time scrubbing, all bodies are editable, different preset systems and experiments, missions to complete (complete 3 to unlock the blackhole object type and mission), and a gallery for user submissions. I would very much appreciate feedback from HN - particularly from the lens of making it engaging and illuminating for students, and anyone that would like to just get a feel for how gravity works. Thank you! [1] https://ift.tt/pbqKZla... [2] https://ift.tt/YyD0z9T https://ift.tt/rglLDpx September 1, 2026 at 11:43PM

Show HN: Semantic Overlays – an NX bit for LLM prompt injection (live demo) https://ift.tt/Qm8YsK4

Show HN: Semantic Overlays – an NX bit for LLM prompt injection (live demo) I've built a new method for steering LLMs called Semantic Overlays, small trained adapters on a frozen model which change how its perceives a piece of its context. The most readily applicable usage is to mitigate prompt injection, and it lets us take a very-injectable Qwen-3.5-9B to SOTA scores on all the prompt injection benchmarks I could find. (They are only blackbox attacks, but I did NOT train on anything like them — whitebox attacks are out of scope for this paper) I'm excited for you to play with the tech — see if YOU can break it! (let me know if you can) Paper at https://ift.tt/Ah9erJX if you want to read more about it, code at https://ift.tt/a9wvnZR , adapters at https://ift.tt/X7VWDKl... Also https://ift.tt/vHP63nw if you wanna watch a little video I made! https://semantic-overlays.vercel.app/ September 2, 2026 at 12:40AM

Show HN: Running 104GB Qwen3.8-Flash-Next on 48GB Mac with at ~12 tok/s https://ift.tt/M4IhHVX

Show HN: Running 104GB Qwen3.8-Flash-Next on 48GB Mac with at ~12 tok/s I built slotstream, a way to run Qwen3.8-Flash-Next 4-bit on a low-memory mac starting from 16GB, a 125B parameter model that would need 100GB+ memory/RAM, thanks to expert-offloading/ssd-streaming. Easy to install/update, and mac-native using MLX and Swift. It ships with auto-mode, which makes a good tradeoff between memory usage and speed. I'll be implementing and porting the MTP module for speculative decoding next https://ift.tt/Nl6hi4y September 1, 2026 at 11:42PM

Show HN: Keel - A conductor, not an agent loop https://ift.tt/at5u3wA

Show HN: Keel - A conductor, not an agent loop https://daneb.github.io/keel/ September 1, 2026 at 03:22AM

Show HN: Guess the AI model from its web design https://ift.tt/DZc4Mvr

Show HN: Guess the AI model from its web design https://ift.tt/wTsmoOY September 1, 2026 at 02:40AM