Noise infusion banned from statistical products published by Census Bureau

Every Frame Perfect

Appreciating Exif

The adder at the heart of Intel's 8087 floating-point chip

<a href="https:&#x2F;&#x2F;digg.com&#x2F;tech&#x2F;ii9xibgn" rel="nofollow">https:&#x2F;&#x2F;digg.com&#x2F;tech&#x2F;ii9xibgn</a>

GLM 5.2 Is Out

AI OSS tool repo goes archived over night after raising $7.3M Seed

I built Paca out of pure passion—a free and lightweight Jira alternative written in Go where humans and AI agents work together as equal teammates to plan sprints and assign tasks to each other. It is fully customizable with custom views, fields, and a WASM-based plugin architecture. My team uses it daily for our own development, so it will be continuously maintained and completely free forever

Show HN: Paca – Lightweight Jira alternative for human-AI collaboration

An Interview with Intel's Kira Boyko: Xeon 6's Product Director

Trophic memory, deer, and a unique scientific object

Shepherd's Dog: A Game by Fable

Automating myself out of development

The computer science degree isn’t dead

Open source AI must win

PwC Report: AI Making Medical Bills Higher

Electric motors with no rare earths

Leaving Mozilla

Tessera – a consent-gated tunnel that's blind to your traffic

There is a shadow hanging over this Fable thing

We’re Connor and Ambar from BitBoard (<a href="https:&#x2F;&#x2F;bitboard.work">https:&#x2F;&#x2F;bitboard.work</a>). BitBoard is an agentic analytics workspace. We give you the infrastructure and visualization layer to analyze data with AI.Today, we’re launching dashboards that you and your agents can work on together. You can connect your coding agent or AI chat to BitBoard and build live reporting. Here’s a demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=HPl0K565a7c" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=HPl0K565a7c</a>.AI tools treat data analysis as ephemeral, making it hard to report or collaborate. Legacy BI tools weren’t intended for AI users, so they bolt on chatbots and can’t offer meaningful control to your agents. Software can now make far more of a business legible than BI ever could, but neither legacy BI nor chat bots are built to handle it.Our original product was AI agents for administrative tasks in healthcare (<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44237769">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44237769</a>), but customers kept pulling us toward their data analysis problems: queries scattered across disparate sources, spreadsheets floating everywhere. We kept building tooling for addressing that, and at a certain point those tools were becoming our product.We ran into several problems. Agents made bad inferences because they had no context on the business. They couldn&#x27;t be trusted to make decisions because nothing checked their work. And anything one agent or one person figured out was invisible to everyone else. In BitBoard, humans and agents interact with the same data primitives but get tools designed for their own work.We’re building dashboards to make the human reading experience better. These dashboards progressively use intelligence - starting from code or SQL queries and leading to full embedded apps. Humans and agents will need to agree on methods to interpret data, so we’re letting both contribute to canonical sources, entities, and measures (using your favorite semantic model or ours). Every answer comes with provenance, and the same call with the same parameters returns the same number.Looking ahead, these shared primitives let long-running agents operate inside a business, and we&#x27;re building those agents too. An agent needs a measurable goal and a way to verify its work. BitBoard gives it both. The agent takes a problem like a metric drifting or a funnel leaking and figures out what to do next. Its work becomes datasets, dashboards, and traces that the team can observe and sign off on.Technically, we’re building a collaboration engine with isomorphic updates for humans and AI, columnar analysis (we use DuckDB and Apache Arrow), grounding and verification infrastructure, and enabling long running tasks with agent containers and traces. For agentic work we’re big fans of applying LLM judgement to discover problems, and then generating deterministic software to automate them.Try it out at <a href="https:&#x2F;&#x2F;app.bitboard.work">https:&#x2F;&#x2F;app.bitboard.work</a>. (We require an email so we can set up your account).We’re excited about how data analysis and science can change in the age of LLMs, and welcome all your thoughts!

Launch HN: BitBoard (YC P25) – Analytics Workspace for Agents

<a href="https:&#x2F;&#x2F;x.com&#x2F;ClaudeDevs&#x2F;status&#x2F;2065597942602531163" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;ClaudeDevs&#x2F;status&#x2F;2065597942602531163</a>

Statement on US government directive to suspend access to Fable 5 and Mythos 5

If you are asking for human attention, demonstrate human effort

Setting up Kafka or such enterprise oriented software with their clusters or dedicated servers is heavy and bothering enough that most small 
teams or indie hackers skip it entirely and making compromise to use in-memory queues.I wanted something in between: a persistent queue that is simple to run (one binary, which makes one sqlite db), gets real fault isolation and crash recovery due to Elixir, easy to inspect (open ezra.db in any SQLite browser and see every 
task), and requires no new client library - it speaks the Redis Streams wire protocol, 
so any Redis client in any language just works out of the box.Very short demo video: [<a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MLYyD3DVWmE" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MLYyD3DVWmE</a>]

Show HN: Lightweight Task queue on Erlang/OTP, SQLite-backed, no overengineering

10:40:36 PM SATURDAY JUNE 13, 2026

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