Show HN: Dally – A little every day adds up https://ift.tt/duFAIY5

Show HN: Dally – A little every day adds up https://mctools.site July 23, 2026 at 03:49AM

Show HN: I created a catalog with embeds for your projects https://ift.tt/h2pIMBn

Show HN: I created a catalog with embeds for your projects Hello HN! Today I would like to introduce my new open-source project called EmbedCatalog. It's free and you can add yours and get interesting and practical embeds there. So far, not many embeds have been done, only three, but in the future I will create more. I would also like to make an editor, but I don’t know how to implement it to make it look better. You may ask “how is this project different from others?” In fact, I wanted to combine the two ideas of a list and the usual README images into one. It turned out how it turned out, but the idea seems like a good one I would appreciate your feedback ^ ^ https://ift.tt/wKPe5JC July 23, 2026 at 01:43AM

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