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In episode 55 of The AI Fix, Gemini thinks a little meth won't hurt, Mark realises what a terrifying 45mph "robot bird" is really for, Graham finds a surprising number of TikTokers in the bible, an AI discovers dust on Mars, Google forgets what year it is, and Apple finally enters the AI chat. Graham finds out what happened when ChatGPT took on 1979's Atari Video Chess at his favourite “sport”, and Mark explains why Apple has been raining on the AI reasoning parade. All this and much more is discussed in the latest edition of "The AI Fix" podcast by Graham Cluley and Mark Stockley.

This $350 smartwatch can outlast its competitors in battery life and fitness features, but may fall short on software updates.

Network security doesn't always require expensive software. Two Linux distributions -- Kali Linux and ParrotOS -- can help enterprises fill in their security gaps.

Article URL: https://endof10.org/

Comments URL: https://news.ycombinator.com/item?id=44299872

Points: 1

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Article URL: https://people.computing.clemson.edu/~malloy/publications/papers/sac08/paper.pdf

Comments URL: https://news.ycombinator.com/item?id=44299869

Points: 1

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Article URL: https://www.theregister.com/2025/06/17/rack_scale_ai/

Comments URL: https://news.ycombinator.com/item?id=44299850

Points: 2

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Article URL: https://lp.jetbrains.com/phpverse-2025/

Comments URL: https://news.ycombinator.com/item?id=44299847

Points: 1

# Comments: 0

Hey HN, I'm Jim – one half of the two-person team building Henosia (https://www.henosia.com/), a browser-based visual vibe coding tool with a purpose-built JavaScript Engine.

Why would we do something as crazy as building our own JavaScript Engine? In a word, speed.

Today's vibe coding is built around files: Edit, save, a build is triggered, Hot Module Refresh triggers a file-level re-evaluation, the page re-renders. This means that code updates are much heavier and slower than they need to be, especially compared to how fast drawing and design tools can perform updates when you edit.

To get design tool like speeds (60FPS) for vibe coding, edit operations need to happen at the lowest possible level: The syntax tree nodes. Changes to the syntax tree then need to cause a minimal amount of code to be re-evaluated in the browser, which is where our purpose-built JavaScript Engine comes into play. Well, technically it's a TypeScript Engine, because it directly evaluates TypeScript syntax trees (ASTs), skipping over non-executable nodes like type annotations.

Here's how it works: The engine receives a change in the AST because the user made a visual edit of a selected component using our component props panel, e.g. to change the size property on a Button component. We're currently focusing on React, so the engine identifies the closest most fine-grained way to perform the rendering update, which in this case is to re-render the nearest parent component that owns the Button component. This happens in just a few milliseconds, and we're able to optimistically perform the change on the client before distributing the change to the server.

Since we're not bound to file save operations and file watchers, we can have multiple pending draft edits at once, e.g. to present the user with multiple AI-based edit suggestions. This capability opens up a ton of new ways to interact with code, with design tools and code editors converging into something entirely new.

If you want to see this thing in action, head over to https://www.henosia.com and drop a prompt to get the project started. Use the selection tool, it's the button with a pen on a bullseye icon next to the chat input. This starts our code engine, and you're able to instantly preview and edit Tailwind styling and component properties at 60FPS. Our docs at https://docs.henosia.com/edit/select have more info, and https://www.youtube.com/watch?v=5yqsFGt7zAg walks through the capabiliies.

I’d love to hear what you think about our approach, and where we should take it next :)

PS: For a limited time our free plan includes unlimited AI messages and visual edits.

Comments URL: https://news.ycombinator.com/item?id=44299833

Points: 1

# Comments: 0

Article URL: https://tldrsec.com/p/phil-venables-rsac-2025

Comments URL: https://news.ycombinator.com/item?id=44299827

Points: 1

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Article URL: https://hackaday.com/2025/06/17/a-gentle-introduction-to-ncurses-for-the-terminally-impatient/

Comments URL: https://news.ycombinator.com/item?id=44299825

Points: 1

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I just saw a new section pop up on my home page, something like "Ask for videos in any way you like" with a few natural language query examples. I tried my own, and in a few seconds 3 highly specific, great videos directly addressing what I asked appeared, with more below.

I believe there's no way I could have found this content through traditional YouTube search, and would have had to have passively waited for it to be recommended, or followed the social/link web to get to it, across long journeys of search country, etc.

This is game changing. Thank you YouTube for putting this in. Really well done!

Comments URL: https://news.ycombinator.com/item?id=44299798

Points: 1

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Article URL: https://en.wikipedia.org/wiki/Sound_Burger

Comments URL: https://news.ycombinator.com/item?id=44299794

Points: 2

# Comments: 0

Hey HN! I built Anime AI Generator , a browser-based tool that lets you generate high-quality anime art instantly—no GPU, Stable Diffusion setup, or coding required.

Key Features: - Preloaded AI models (Pony Diffusion, Animagine, Illustrious) for diverse styles - Text-to-anime in seconds – just describe your idea (e.g., "cyberpunk samurai with neon katana") - Zero local setup – runs entirely in the browser - Free trial to test before upgrading

Why? Existing tools often require technical expertise setup or expensive hardware. I wanted a frictionless way for anyone to create anime art—whether for fun, prototyping, or professional projects. (Bonus: Works on mobile too!)

Would love to hear your thoughts, use cases, or feature requests. Thanks!

Comments URL: https://news.ycombinator.com/item?id=44299774

Points: 1

# Comments: 0

Article URL: https://udara.io/science/virtual-cells/

Comments URL: https://news.ycombinator.com/item?id=44299742

Points: 1

# Comments: 1

Magic Machines lets you transform images instantly with curated styles and effects. Pick a Machine, drop in your image, and watch it work — no prompts, no friction.

Launch Machines:

- Ghibli — Turn any image into a dreamy Ghibli-style scene.

- Caricature — Transform any image into a fun caricature.

- Vintage Advertisement — Convert your image into a retro ad poster.

- Victorian Era Portrait — Create timeless Victorian-style portraits.

- Muppet — Reimagine any photo in the style of The Muppets.

- Restore — Repair old, damaged, or low-quality images.

- Baby Generator — Combine two faces to see what their baby might look like.

- ... New Machines drop regularly, so check back often to see what’s new.

And images are just the first step — wait until you see what our Machines do next.

Comments URL: https://news.ycombinator.com/item?id=44299741

Points: 2

# Comments: 0

Last year, I needed to find all software companies in São Paulo for a project. The good news: Brazil publishes all company registrations as open data at dados.gov.br. The bad news: it's 85GB of ISO-8859-1 encoded CSVs with semicolon delimiters, decimal commas, and dates like "00000000" meaning NULL. My laptop crashed after 4 hours trying to import just one file.

So I built a pipeline that handles this mess: https://github.com/cnpj-chat/cnpj-data-pipeline

THE PROBLEM NOBODY TALKS ABOUT

Every Brazilian startup eventually needs this data - for market research, lead generation, or compliance. But everyone wastes weeks: - Parsing "12.345.678/0001-90" vs "12345678000190" CNPJ formats - Discovering that "00000000" isn't January 0th, year 0 - Finding out some companies are "founded" in 2027 (yes, the future) - Dealing with double-encoded UTF-8 wrapped in Latin-1

WHAT YOU CAN NOW DO IN SQL

Find all fintechs founded after 2020 in São Paulo:

SELECT COUNT(*) FROM estabelecimentos e JOIN empresas emp ON e.cnpj_basico = emp.cnpj_basico WHERE e.uf = 'SP' AND e.cnae_fiscal_principal LIKE '64%' AND e.data_inicio_atividade > '2020-01-01' AND emp.porte IN ('01', '03');

Result: 8,426 companies (as of Jun 2025)

SURPRISING THINGS I FOUND

1. The 3am Company Club: 4,812 companies were "founded" at exactly 3:00:00 AM. Turns out this is a database migration artifact from the 1990s.

2. Ghost Companies: ~2% of "active" companies have no establishments (no address, no employees, nothing). They exist only on paper.

3. The CNAE 9999999 Mystery: 147 companies have an economic activity code that doesn't exist in any reference table. When I tracked them down, they're all government entities from before the classification system existed.

4. Future Founders: 89 companies have founding dates in 2025-2027. Not errors - they're pre-registered for future government projects.

5. The MEI Boom: Micro-entrepreneurs (MEI) grew 400% during COVID. You can actually see the exact week in March 2020 when registrations spiked.

TECHNICAL BITS

The pipeline: - Auto-detects your RAM and adapts strategy (streaming for <8GB, parallel for >32GB) - Uses PostgreSQL COPY instead of INSERT (10x faster) - Handles incremental updates (monthly data refresh) - Includes missing reference data from SERPRO that official files omit

Processing 60M companies: - VPS (4GB RAM): ~8 hours - Desktop (16GB): ~2 hours - Server (64GB): ~1 hour

THE CODE

It's MIT licensed: https://github.com/cnpj-chat/cnpj-data-pipeline

One command setup: docker-compose --profile postgres up --build

Or if you prefer Python: python setup.py # Interactive configuration python main.py # Start processing

WHY OPEN SOURCE THIS?

I've watched too many devs waste weeks on this same problem. One founder told me they hired a consultancy for R$30k to deliver... a broken CSV parser. Another spent 2 months building ETL that processes 10% of the data before crashing.

The Brazilian tech ecosystem loses tons of hours reinventing this wheel. That's time that could be spent building actual products.

COMMUNITY RESPONSE

I've shared this with r/dataengineering and r/brdev, and the response has been incredible - over 50k developers have viewed it, and I've already incorporated dozens of improvements from their feedback. The most common reaction? "I wish I had this last month when I spent 2 weeks fighting these files."

QUESTIONS FOR HN

1. What other government datasets are this painful? I'm thinking of tackling more.

2. For those who've worked with government data - what's your worst encoding/format horror story?

3. Is there interest in a hosted API version? The infrastructure would be ~$100/month to serve queries.

The worst part? This data has been "open" since 2012. But open != accessible. Sometimes the best code is the code that deals with reality's mess so others don't have to.

Comments URL: https://news.ycombinator.com/item?id=44299733

Points: 1

# Comments: 0

Article URL: https://www.stainless.com/blog/from-api-to-mcp-a-practical-guide-for-developers

Comments URL: https://news.ycombinator.com/item?id=44299718

Points: 1

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Article URL: https://www.quora.com/Why-is-SO2-not-considered-a-major-greenhouse-gas

Comments URL: https://news.ycombinator.com/item?id=44299699

Points: 2

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Article URL: https://www.reuters.com/business/google-scale-ais-largest-customer-plans-split-after-meta-deal-sources-say-2025-06-13/

Comments URL: https://news.ycombinator.com/item?id=44299693

Points: 1

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