Show HN: Wi-Fi Remote for My AC on an M5StickC, in Rust https://ift.tt/pUlGNfZ

Show HN: Wi-Fi Remote for My AC on an M5StickC, in Rust https://ift.tt/IXuqEQP July 22, 2026 at 04:19AM

Show HN: Agent in 9 Lines Python https://ift.tt/iH5o7Es

Show HN: Agent in 9 Lines Python I asked myself: what would a minimal implementation of an agent look like? Something that works out of the box, is a real agent with tool calling, but without 1000s of lines of code, without dozens or hundreds of npm or pypi dependencies. Something with just a few 'essential' features (not a whole kitchen sink that most agent harnesses come with nowadays). An implementation close to pseudocode that you can look at in one page, everything there at a glance, no scrolling. This is the agent.py I ended up with so far: import json,sys;from subprocess import getoutput as sh;from urllib.request import Request as R,urlopen url=sys.argv[1];h=[];b=dict(model="gpt-5.6",input=h,tools=[dict(type="custom",name="sh")]) while p:=input("> "): h+=[dict(role="user",content=p)];H={"Content-Type":"application/json"} while True: o=(r:=json.load(urlopen(R(url,json.dumps(b).encode(),H))))["output"] h+=o;c=[i for i in o if i["type"]=="custom_tool_call"];z=r["usage"]["total_tokens"]/10500 if not c:print(o[-1]["content"][0]["text"],f'\n[{z:06.3f}%]');break h+=[dict(type="custom_tool_call_output",call_id=i["call_id"],output=sh(i["input"])) for i in c] It is a bit code golfed but I think it is fairly readable - imports are all from stdlib (0 external dependencies!) - assumes there is an inference api endpoint running somewhere - assumes the inference api endpoint is openai-like - model hardcoded to "gpt 5.6" (=> Sol), can easily be changed to e.g. open weight (kimi k3, glm 5.2 etc) - api endpoint url is passed as arg to the python script - configures only 1 custom tool: 'sh' - 'sh' is sufficient for interacting with the environment in an open ended way - new api output gets added to history ("h") - if api output contains tool calls the tool calls get executed - agent gives control back to user when the last model response is without tool calls - agent message to user shows % of context window used Noteworthy: no dependencies other than python stdlib (!) - less startup time - less dependency churn - less supply chain attack vector surface - less code to verify and understand no mcp, no plugins, no security theater - if you want to add something specific: add it explicitly - adapt the environment to give the agent access or restrict access to tools, resources, network etc (the env is the security boundary, not the harness) no system prompt - every token in context window is precious - current strong models do fine without steering via system prompt (or are even harmed by long overly specific system prompts designed for models from months ago) - system prompt or agents.md context can easily be added if needed (agent can also discover it or get prompted to read from environment as is) how to run/deploy the agent - design the environment you want to give the agent (container, docker, sandbox of your choice) - start an inference api endpoint that is openai-like (support the request/response shape used in agent.py above) - inference api endpoint can be as simple as a proxy to openai api that adds credentials/api key - adapt as you want/need it, change the model, remove/alter context window behaviour, add tools, etc etc Looking for any feedback you have to make it more clear or even simpler! https://gist.github.com/tosh/6e91a9dbf08dd630c535e7345ac7f0b5 July 22, 2026 at 03:52AM

Show HN: Ipek – a visual IDE for workflow automations https://ift.tt/swFcIVz

Show HN: Ipek – a visual IDE for workflow automations Hi HN, I'm Nuru. Ipek is a desktop app that runs file and API automation workflows locally. It actually resulted from my laziness: automating API calls, Excel/CSV parsing, OCR and LLM calls by hand takes too much time to maintain, especially when various teams request additional tools on top of those scripts. So I built a visual workflow builder where my non-technical teams can create & run those deterministic workers themselves, see the logs, and produce the outputs on their own machines. They can also connect an agent over localhost MCP and vibe code new workers; what comes out is real Kotlin that compiles and plugs into the workflow. An agent can drive the whole app, which is great for iterating on workflow building. There is no server side, it's a JVM app (not Electron! :D) that lets you create, compile & attach your own workers; it's basically an IDE for automations. You can create workers either from your own agent over localhost MCP (Claude Code, Codex) or using the builtin Claude chat with your own API key — so Ipek is completely free to run, with no per-task fees; your only cost is your own API usage, and you own the data & software. Check the video on the website for more. Current state, honestly: it's quite a stable alpha, available for macOS and Windows; Linux is on its way. I also want to implement triggers, schedules, cron jobs, and deploying a workflow to the CI/CD of your choice, in case the workflows/data gets heavy. Tech stack: Kotlin Compose Multiplatform on the JVM, SQLDelight, workers are Gradle-built plugins that hot-load — so each worker is actually a self-sustainable(?) JAR executable that attaches like a JetBrains IDE plugin, then Ipek orchestrates and executes them. I think Ipek will be useful for many of you, too; I'd like to hear which repetitive tasks you would actually throw at it. Cheers! https://ipek.run/ July 22, 2026 at 02:51AM

Show HN: Dex – Explore any Data Warehouse without worrying about costs https://ift.tt/apZj9VN

Show HN: Dex – Explore any Data Warehouse without worrying about costs https://ift.tt/6iAotXk July 22, 2026 at 01:05AM

Show HN: Explore 6048 YC companies as an interactive galaxy https://ift.tt/qAkuSCQ

Show HN: Explore 6048 YC companies as an interactive galaxy https://ift.tt/h6B5tqe July 19, 2026 at 04:15AM

Show HN: Competiflow – monitor competitor pricing, changelogs, blogs https://ift.tt/EBvORni

Show HN: Competiflow – monitor competitor pricing, changelogs, blogs Hi, I'm the author. I run two small products I launched this year and as a daily task I was keeping an eye on my competitors. Two things that I was particularly curious about were: the new features they are launching and the content they are posting. I was initially checking their blogs manually and handpicked the topics I was curious about, then finding new angles, proper keywords. Same for the features - skimming the changelogs, looking at the homepages and searching for mentions of any new products. This is a boring task that takes time. But I had to do it daily because I wanted to keep my products up with the competition. So I started to automate it. A few cron jobs, a few parsers. But still, there was the manual part like reviewing the changes, and separating the signal from the noise. There was a lot of noise like cookie banners, CSS changes. But it was working decently and I was okay with that. Later I got curious about the idea of tracking the pricing pages. And here is where my automation started to break. Compared to blogs and changelogs, the pricing pages have unpredictable formats. It's practically impossible to write a parser that will work reliably and get all the useful information. Here I tried to send the pricing page content to LLM, hoping that it will get the useful information and ignore all the noise. It wasn't cheap and wasn't reliable. I guess because LLMs are not deterministic and they can return different data from the same input. Garbage in, garbage out. Then I started looking for any products that would help me fix all these problems and the first one I stumbled upon was Visualping. Pretty neat tool, but after a few days of using it - I realized that I'm not making my work easier. I still have to review the changes manually and decide what to do about them. This is the moment I realized that I may not be the only one who has this problem and I started digging deeper into my automation scripts. It took me about a month to build something that I can actually use. I'm still working daily trying to get rid of as much noise as possible because the more websites I run through it - the more edge cases I find. But it's getting there, so I'm pretty optimistic. That's why I'm sharing it with you. Here it is: https://competiflow.com You paste a homepage, it discovers pricing/docs/blog/changelog URLs, re-reads them on a schedule, extracts fields like plan names and prices, and diffs those instead of raw pixels. When something real changes you get a short summary plus the before/after text and the source URL. If it's a blog post - it generates relevant keywords and looks at the search volume, so you know if this post is worth looking at. There's a REST API and MCP server too. I use the MCP from Cursor when I'm working on other stuff. It has a free trial so you can test it out without spending any money. Happy to answer any questions. https://ift.tt/8D3iuY0 July 21, 2026 at 02:15AM

Show HN: QuiverKit – 62 developer tools that run offline https://ift.tt/UXEseBg

Show HN: QuiverKit – 62 developer tools that run offline I’m currently in the process of getting used to writing code with AI. In addition, I was curious about desktop app development. I came across Tauri and wanted to give it a try. That’s how this hobby project came about. It was important to me that the inputs not go to a server. I wanted to build a project focused on that. For now, it includes 62 different tools. It has been released as native apps for Windows and Linux. The web version is already available. It’s also currently undergoing review as a browser extension. From a Vibecoding experience perspective, I think projects like this are great for warming up. They’re also a great opportunity to experiment with different technologies we’re not familiar with. https://quiverkit.dev July 20, 2026 at 10:07PM