Show HN: Groot – Kubernetes incident evidence in one .tar.gz https://ift.tt/ln9rjNb

Show HN: Groot – Kubernetes incident evidence in one .tar.gz https://ift.tt/4OyaLKE July 4, 2026 at 07:31PM

Show HN: Dockside – I turned unused space around the macOS Dock into a workspace https://ift.tt/GUCEmhY

Show HN: Dockside – I turned unused space around the macOS Dock into a workspace https://ift.tt/YUzr7Ou July 4, 2026 at 01:05AM

Show HN: Mcpsnoop – Wireshark for MCP (transparent proxy and live TUI) https://ift.tt/Yz7Ddt5

Show HN: Mcpsnoop – Wireshark for MCP (transparent proxy and live TUI) https://ift.tt/VIqWCAd July 3, 2026 at 11:53PM

Show HN: Ultracodex – Run Claude Ultracode Dynamic Workflows with Codex Agents https://ift.tt/v1NKdIB

Show HN: Ultracodex – Run Claude Ultracode Dynamic Workflows with Codex Agents Claude Fable has been incredible, however the plan usage runs out too fast, especially if you use ultracode mode (Claude Code's workflow feature, where the model writes small JavaScript programs that orchestrate subagents) and let the agents go brrr. I want Fable to focus on high-value tasks such as planning or verification, and not burn tokens on mundane implementation work. So I built ultracodex, an engine that spawns codex agents to complete the same ultracode workflows and hand off seamlessly to and from Claude. It runs the same workflow scripts that Claude writes. I designed this tool for anyone owning both Claude and Codex subscriptions or anyone who enjoys Fable and wants to make the quota last. This tool is in early days but I am very excited about the potential. I think it’s also a great “loop engineering” starter pack for us because 1) Claude is great at writing loops and 2) ultracodex runs the loops with Codex agents - given their generous limits, you don’t have to worry about usage much. So “loop engineering” is no longer a mysterious thing but a tool you can throw at any task. I have some examples of “loops” in my repo as well. Project is completely open-source. Apache-2.0, npm i -g ultracodex, demo video in the README. Please check it out! https://ift.tt/9xTyXUa July 3, 2026 at 10:45PM

Show HN: Bais.News – News Neutralization Piepline https://ift.tt/3swrRvj

Show HN: Bais.News – News Neutralization Piepline Solo dev here, been working on this project the last 7 months and I need some feedback. Bais aggregates news stories from a broad range of sources and synthesizes an article focused on reporting the overlapping coverage of all sources. The goal being to provide a fact first account while eliminating narrative spin and emotionally charged language. Happy for any feedback, thanks. https://bais.news July 3, 2026 at 12:29AM

Show HN: QUALITY.md – open format/specification, agent skill, and CLI https://ift.tt/kM8wfnE

Show HN: QUALITY.md – open format/specification, agent skill, and CLI Hello all, I created QUALITY.md to help build a holistic quality evaluation process for my projects. Turns out it's also ideal for loop engineering. I'm hoping this provides a valuable contribution to the conversation around quality and craft and having AI help us in the effort. I hope to shift the mindset from a reactive/review/repair mindset to a proactive care mindset. Give it a go. I look forward to your thoughts/comments/feedback! Website: https://getquality.md GitHub: https://ift.tt/IZQnupq https://getquality.md July 2, 2026 at 11:36PM

Show HN: I built an MCP to allow Claude to pair-program with another Claude https://ift.tt/AnaYXQ7

Show HN: I built an MCP to allow Claude to pair-program with another Claude https://ift.tt/uiOVWcR July 2, 2026 at 11:32PM

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