Show HN: AnalystAIPack – 118 runnable agent skills for malware analysis and RE https://ift.tt/yLkfH3b

Show HN: AnalystAIPack – 118 runnable agent skills for malware analysis and RE https://ift.tt/aBT2VRZ July 2, 2026 at 12:27AM

Show HN: Classify mechanical faults using Contrastive Language-Audio Pretraining https://ift.tt/D76p3c4

Show HN: Classify mechanical faults using Contrastive Language-Audio Pretraining https://ift.tt/5wYBUks July 1, 2026 at 11:57PM

Show HN: PMB – local memory for coding agents that shows if it is used https://ift.tt/4B2IPmg

Show HN: PMB – local memory for coding agents that shows if it is used https://pmbai.dev June 29, 2026 at 10:37PM

Show HN: NodePad – AI agent on a canvas instead of a linear chat https://ift.tt/gTtZpc6

Show HN: NodePad – AI agent on a canvas instead of a linear chat https://node-pad.com/ June 30, 2026 at 07:47PM

Show HN: My 13-year-old built an ant colony tracker https://ift.tt/YoKwM72

Show HN: My 13-year-old built an ant colony tracker He's 13 years old. He wanted to track his own ant colonies — growth, feeding, humidity, and other metrics. He built the whole app himself with some help from AI tools; I just helped him deploy it to a server. Would love to hear your feedback! https://formicarium.es June 30, 2026 at 11:48PM

Show HN: fenic – LLMs as dataframe operators, query meaning and structure https://ift.tt/6DG5azb

Show HN: fenic – LLMs as dataframe operators, query meaning and structure Hey friends. I'd like to share a project that's dear to me. fenic is a dataframe API with LLMs added as first-class citizens, a classic lazy dataframe API extended with new operators that are backed by LLMs. What this gets you is the ability to work with structured and unstructured data in the same context. Most importantly, the LLMs aren't integrates as opaque UDF black boxes. They're exposed as "semantic" operators that the planner can reason about alongside the classic ones. (There are examples and code snippets on the repo to see how everything works together) Why build this? I'm a data infra / systems person. When LLMs showed up, what I saw was a new type of compute that changes the characteristics of the workloads we deal with. I wanted to experiment with how our current systems can absorb these new workloads and compute types, and what it would take to make the DX as seamless as possible, that's where the UDF + arbitrary prompt was feeling too problematic. To support this properly, we had to introduce a few really cool things: New plan operators. You don't just send prompts at an LLM. You use operators like semantic join, semantic map and reduce, and semantic filter, among others. They mix with the classic operators, and because the planner sees them as real operators rather than black boxes, it can reorder work around them. Typed outputs. There's ergonomics to turn the output of a semantic operator straight into a typed dataframe column. A Pydantic schema for the LLM output becomes a typed struct column you can unnest, explode, and so on. New data types like a markdown data type. Markdown became an important way to share information with LLMs, even though it started life as a way to format text for presentation. It carries structure, and being able to access that structure the way you would a struct or JSON type adds to the developer experience I mentioned. Async UDFs. One of the more interesting shifts in workloads from the LLM explosion is the need to put heavily I/O-bound steps in your pipeline: fetching a response from an API, crawling a website, and so on. Async UDFs fill that gap, and the implementation handles the nuances for you: concurrency, retries, and the rest. An LLM-inference-aware planner and runtime. This is one of the parts I'm most excited about, and there's a lot still to do. Today: identical prompts within a batch collapse to a single model call, so duplicates cost zero tokens; requests are dispatched concurrently under per-provider rpm/tpm limits with retries and backoff; null and empty cells skip the model entirely; and you get token and cost metrics per operator. There's also an optional persistent response cache so re-runs skip the model. MCP as a new catalog primitive. Much like a registered view, you can register a dataframe pipeline as an MCP tool in the catalog. fenic then serves an MCP server with that pipeline as the tool's logic, executed over your data. These are just some of what's gone into fenic while experimenting with how LLMs can become part of our compute infrastructure. There's more, and plenty more to polish on what's already there. I've been using fenic for all sorts of things. On the small/personal end, I use it to take my podcast audio recordings and turn them into nicely structured tables of metadata I can research. On the heavier end, I use it as tooling for agents to analyze agent traces exported from Pydantic Logfire, to discover evals and turn them into reproducible artifacts in the form of dataframe pipelines. pip install fenic Repo: https://ift.tt/VAiubM9 Docs: https://docs.fenic.ai There's also a skill you can use with claude code, codex etc. to quickly get started with fenic in your favourite agentic coding environment. I'd love to hear your thoughts, criticism, and anything else that comes to mind. I'm here to answer questions. https://ift.tt/VAiubM9 June 30, 2026 at 11:39PM

Show HN: Openleetcode – local LeetCode runner with open test suites https://ift.tt/cLrsW2p

Show HN: Openleetcode – local LeetCode runner with open test suites https://ift.tt/ztMyqw1 June 30, 2026 at 11:16PM

Show HN: The UNESCO Tsunami Warning Emails Are Gone https://ift.tt/ip9Kotv

Show HN: The UNESCO Tsunami Warning Emails Are Gone This key piece of tsunami warning and safety was discontinued this morning and evidently there's no way to get it back. :/ https://ift.tt/pBus8xG June 30, 2026 at 01:06AM

Show HN: Rust / Red Alert inspired WASM game in the browser (open source) https://ift.tt/jFaShpc

Show HN: Rust / Red Alert inspired WASM game in the browser (open source) https://punnerud.github.io/mpe-ra/ June 29, 2026 at 11:48PM

Show HN: HyperPaste – a free, open-source clipboard manager for macOS https://ift.tt/VqjPIrm

Show HN: HyperPaste – a free, open-source clipboard manager for macOS To me it always felt like clipboard history was one of the few things missing from macOS. There are already some excellent clipboard managers available, but after trying quite a few of them I kept coming back to the same goal: I wanted something that felt like it belonged on macOS. Fast, keyboard-first, private, and visually restrained. One thing I felt strongly about from the beginning was that it had to be open source. Clipboard managers have access to some of the most sensitive data we copy — passwords, API keys, personal information, financial details — and for something that sits in the background watching my clipboard all day, I personally wasn't comfortable using closed-source software. That ruled out a lot of otherwise great applications for me. The result is HyperPaste. HyperPaste is a free, open source clipboard manager for macOS, built with SwiftUI and AppKit. Clipboard data stays on your Mac — there are no accounts, cloud services, telemetry, or ads. Some highlights: - Instant search - Keyboard-first workflow - Rich support for text, code, links, colors, images, and files - Automatic color previews - Favorites for frequently used clipboard items - Native context menus and keyboard shortcuts - Local-first design with no ads or analytics One of the design goals was to avoid turning clipboard history into a productivity suite. I wanted HyperPaste to stay focused on one job: helping you quickly find and reuse things you've copied. The project is MIT licensed and the source code is available on GitHub. I'd genuinely appreciate any feedback, bug reports, feature suggestions, or criticism. I'll be around to answer questions throughout the day. https://hyperpaste.io June 29, 2026 at 10:13PM

Show HN: Sonar, local cited codebase briefings tailored to your role https://ift.tt/GTERy9V

Show HN: Sonar, local cited codebase briefings tailored to your role https://ift.tt/8hLglpq June 29, 2026 at 07:47PM

Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch https://ift.tt/4bG8IU9

Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch Hi everyone, I started working on nanoeuler after the ban of anthropic's fable because my ambition and dream is to work in the AI field in anthropic. The two interesting reasons that led me to create nanoeuler were (1) interfacing with llm does not mean understanding how they are composed and (2), working on llm with a very low-level layer to understand the correlation between parameters and data and growth of the model and how the GPU works and how some layers can be optimized. So I started working on it with a research aspect by making nanoeuler grow more and more but doing one step after another starting from Shakespeare.txt and understanding what a text generation model understands at 23 million parameters. For example, nanoeuler at that number had understood that Name: started a line and wrote that line with sense. I wrote everything in CUDA because I wanted to not use any intermediary between the model in training and inference and what it had to do. Then the use of SFT and much more, even if in small ways, were really useful to understand the various step to make an llm like a chatbot.Any feedback, help, or suggestions are absolutely welcome! https://ift.tt/RSq9ecG June 29, 2026 at 02:38AM

Show HN: Caliper – pass@k reliability testing for Claude Code and Codex skills https://ift.tt/XOrKvC8

Show HN: Caliper – pass@k reliability testing for Claude Code and Codex skills Skills for Claude Code and Codex are hard to test. What I mean by hard is that there's no standard way to do it. You evaluate the skill once on something, it looks like it works. You publish it. Then the new super model releases (GLM 5.2 anyone?), it will quietly break for some part, and you won't find out until your users complain. I also faced the same problem, so I tried to build something lightweight to stop doing that. Caliper. It's a local and lightweight harness that runs a skill k times in isolated environments and gives you a pass@k score (How much times it succeeded in these k times). As a non-deterministic technology, you can't just say "it worked once". You need to answer how much it passed in k times. You define success in a YAML spec. I picked YAML to keep a schema and make it still readable for a human. You either use a LLM judge, a Python assertion, or both: Here's an simple evaluation example with a JSON extraction, so you write this in a YAML file: tasks: - name: Extracts action items as clean JSON prompt: "Read /tmp/transcript.txt and write the action items to /tmp/actions.json." expect: "A valid JSON array where every item has owner, task, due. No markdown fences." assert: | import json items = json.load(open("/tmp/actions.json")) assert isinstance(items, list) assert all({"owner","task","due"} <= i.keys() for i in items) Then with the CLI, you'll run it: caliper run extract-actions.eval.yaml --k 5 --baseline What's cool about the --baseline flag is that it will re-runs everything without the skill, so you can see whether the skill is doing the work or the base agent was going to pass anyway: ID Task k(5) pass@k task-1 Extracts action items as JSON 5/5 100% PASS With skill 100% No skill 60% Delta +40% Most models know how to get the JSON right most of the time (JSON extraction was solved by 2 years old already). But that's it, "most of the time" is the bug. That delta shows how the skill actually helped. (It's sometimes 0%, sometimes -100%!) I also created two skills you can get started right away with your favorite harness, e.g. Claude Code, Codex or Pi: - evaluate-skill: run and manage evals without leaving your workflow - grill-skill: reads your SKILL.md, interviews you about what "good" looks like, writes a 3-task spec (happy path, edge case, adversarial), and runs it You can install the skill with the command: npx skills@latest add edonadei/caliper I for now support claude-code, codex, pi, claude-api, openai-api. You can run the agent and the judge as separate backends, so you can run a skill on one and judge with another. GitHub: https://github.com/edonadei/caliper PyPI: https://pypi.org/project/caliper-eval/ Of course, it's a first step. I think the autorater layer can be vastly improved, more handholding to create and iterate on evaluation specs, supporting more harness, why not including this layer into a self-improvement bigger system? If you're also building agentic evaluations, I'm genuinely interested to hear how you are handling that. https://github.com/edonadei/caliper June 29, 2026 at 12:42AM