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Latest AI tool releases, research breakthroughs, and industry news.
Yesterday
sqlite-utils 4.0rc2, mostly written by Claude Fable
I wrote about the sqlite-utils 4.0rc1 release a couple of weeks ago. Since we only have Claude Fable on our Max subscriptions for a few more days, I decided to see if it could help me get to a 4.0 stable release that I felt truly comfortable about, since I try to keep to SemVer and like my incompatible major versions to be as rare as possible. I started with this prompt, in Claude Code for web on my iPhone: Final review before shipping a stable 4.0 release - very important to spot any last minute things that would be a breaking change if we fix them later Here's that initial report it created for me. There were some significant problems that I hadn't myself encountered yet - 5 that Fable categorized as "release blockers". Here's the worst of the bunch: 1. delete_where() never commits and poisons the connection (data loss) Table.delete_where() ( sqlite_utils/db.py:2948 ) runs its DELETE via a bare self.db.execute() with no atomic() wrapper — compare Table.delete() at db.py:2944 , which wraps correctly. The connection is left in_transaction=True , so every subsequent atomic() call takes the savepoint branch ( db.py:430-440 ) and never commits either. Reproduced end-to-end: db = sqlite_utils . Database ( "dw.db" ) db [ "t" ]. insert_all ([{ "id" : i } for i in range ( 3 )], pk = "id" ) db [ "t" ]. delete_where ( "id = ?" , [ 0 ]) # conn.in_transaction is now True db [ "t" ]. insert ({ "id" : 50 }) db [ "u" ]. insert ({ "a" : 1 }) db . close () # Reopen: rows are [0, 1, 2] — the delete, row 50, AND table u are all gone. That's a really bad bug! Very glad I didn't ship that, although at least it would have been a bug I could fix in a 4.0.1 point release, not a design flaw that would force a 5.0. Over the course of 37 prompts, 34 commits and +1,321 -190 code changes over 30 separate files, we worked through the entire set of feedback in turn, making several other design improvements along the way. A weird thing about coding agents is that harder tasks like this one actual
sqlite-utils 4.0rc2
Release: sqlite-utils 4.0rc2 See sqlite-utils 4.0rc2, mostly written by Claude Fable (for about $149.25) .
Building a World Map with only 500 bytes
Building a World Map with only 500 bytes Iwo Kadziela (assisted by Codex) figured out a way to generate a credible ASCII world map using 445 bytes of data: The key trick is to use deflate compression, which is then wired together using this neat snippet of JavaScript. I didn't know you could use fetch() with data: URIs like this: fetch('data:;base64,1ZpLsgIxCEXnrM...==').then( r => r.body.pipeThrough(new DecompressionStream('deflate-raw')) ).then( s => new Response(s).text() ).then( t => b.innerHTML = ' ' + t ) Via Hacker News Tags: ascii-art , data-urls , javascript
Better Models: Worse Tools
Better Models: Worse Tools Armin reports on a weird problem he ran into while hacking on Pi: The short version is that newer Claude models sometimes call Pi’s edit tool with extra, invented fields in the nested edits[] array. And not Haiku or some small model: Opus 4.8. The edit itself is usually correct but the arguments do not match the schema as the model invents made-up keys and Pi thus rejects the tool call and asks to try again. That alone is not too surprising as models emit malformed tool calls sometimes. Particularly small ones. What surprised me is that this is getting worse with newer Anthropic models as both Opus 4.8 and Sonnet 5 show it but none of the older models. In other words, the SOTA models of the family are worse at this specific tool schema than their older siblings. Armin theorizes that this is because more recent Anthropic models have been specifically trained (presumably via Reinforcement Learning) to better use the edit tools that are baked into Claude Code. This has the unfortunate effect that other coding harnesses, such as Pi, may find that their own custom edit tools are more likely to be used incorrectly. Claude's edit tool uses search and replace . OpenAI's Codex uses an apply_patch mechanism instead , and OpenAI have talked in the past about how their models are trained to use that tool effectively. Does this mean third-party coding harnesses like Pi should implement multiple edit tools just so they can use the one with the best performance for the underlying model the user has selected? Tags: armin-ronacher , ai , openai , generative-ai , llms , anthropic , llm-tool-use , coding-agents , pi
New Google commercial imagines a Declaration of Independence written with help from AI
Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?
Midjourney wants Hollywood studios to reveal the details of their AI usage
As part of an ongoing legal dispute with three Hollywood studios, Midjourney is seeking to compel those studios to reveal how they use AI themselves.
Alibaba reportedly bans employees from using Claude Code
Alibaba has reportedly classified Claude Code as high-risk software.
What is Mistral AI? Everything to know about the OpenAI competitor
Mistral AI, which offers some open source AI models, has raised significant funding since its creation in 2023, with the ambition to “put frontier AI in the hands of everyone.”
The fanfiction community is at war with AI — and itself
Fanfiction communities are trying to hunt down writers who haven’t written works with their own hands. | Image: Álvaro Bernis / The Verge Over the past week, a new fanworks movement has kicked off, with the aim to root out authors using generative AI. But the detection methods being implemented are questionable, and any fanfic writer could be caught in the crossfire. Broad distaste around the use of Claude, ChatGPT, and other AI tools has long been a thing in creative communities, including the world of fanfiction. Readers and writers have passed around tips for spotting supposedly AI-generated works, citing anything from em dashes to the broad concept of purple prose. But on June 29th, an anonymous X account called @heatedrivalryai promised a seemingly more reliable solution … Read the full story at The Verge.
Earlier this week
Open Source AI Gap Map
Open Source AI Gap Map Current AI is "a global partnership building a public option for AI", founded as a non-profit at the AI Action Summit in Paris in February 2025 and backed by serious capital ($400m already committed). They launched their Gap Map a couple of days ago - an attempt at indexing the current state of open source AI: The Gap Map v0.1 details 421 products in depth: 266 software tools and libraries, 85 models, 50 datasets, and 20 hardware projects, produced by 228 organizations. These products are organized into 14 categories across 3 layers of the stack (model components, product / UX, and infrastructure). The remaining 24,400 artifacts constitute the uncategorized long tail of the open source AI ecosystem, and will carry no score until they are researched and cited. The map itself is interesting to explore, but I'm more excited about the underlying data - released under an MIT license in the currentai-org/os-ai-map GitHub account: 1,184 YAML files plus the notebooks, schemas and other scripts used to help gather them. Since the files are on GitHub you can use Datasette Lite to explore some of them - here are 16,185 GitHub repos the project is tracking as a CSV file loaded into Datasette Lite. Tags: open-source , ai , datasette-lite , generative-ai , local-llms , llms
Quoting Josh W. Comeau
I just launched my third course, Whimsical Animations, and so far, it’s on track to sell roughly ⅓ as many copies as a typical course launch. It’s a similar story with my two existing courses. Sales are down significantly from last year. There are likely a lot of reasons for this, but I think the biggest is AI. There’s sort of a double whammy with AI: Many people are wondering whether developer jobs will even exist in a few months, so they’re reluctant to spend time/money learning new dev skills. Even if they do want to learn new dev skills, LLMs can provide personalized tutoring, so there’s less incentive to buy a paid course. [...] I’ve spoken to a few course creators now, and we’re all seeing the same trend. Revenue down 50%+. Fewer people engaging with our content. People switching to LLMs, which slurp up all of our work and regurgitate it, without consent or compensation. — Josh W. Comeau , via Salma Alam-Naylor Tags: ai-ethics , llms , ai , generative-ai , careers , josh-comeau
The only AI glossary you’ll need this year
The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.
Fable's judgement
One of the most interesting tips I got from the Fireside Chat I hosted with Cat Wu and Thariq Shihipar from the Claude Code team at AIE on Wednesday was to let Fable (and to a certain extent Opus) use their own judgement rather than dictating how they should work. The example they gave was testing. You can tell Fable "only use automated testing for larger features, don't update and run tests for small copy or design changes" - but it's better to just tell Fable to use its own judgement when deciding to write tests instead. Jesse Vincent just gave me a related tip to help avoid burning too many of those valuable Fable tokens in the few days we have left before the prices go up. Tell Fable to use other models for smaller tasks, applying its own judgement about which model to use. I prompted Claude Code just now with: For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent Claude saved this memory file in ~/.claude/projects/name-of-project/memory/delegate-coding-to-subagents.md : --- name: delegate-coding-to-subagents description: Simon wants coding tasks delegated to subagents running an appropriately lower-power model metadata: node_type: memory type: feedback originSessionId: 30068d78-43a9-4fb1-bb29-9799e18c526a --- Stated by Simon on 2026-07-03: "For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent." Why: cost/efficiency — implementation work rarely needs the top-tier model; judgment, review, and synthesis stay with the main loop. How to apply: when a task in this project is primarily writing/editing code, spawn an Agent with a model override (sonnet for substantive implementation, haiku for trivial/mechanical edits) and a self-contained prompt; review the result in the main loop before committing. Design, auditing, data synthesis, and anything judgment-heavy stays in the main model. See also [[project-goals]]. So far it seems to be working well. I'm getti
The browser wars aren’t about search anymore — here are the best alternatives to Chrome and Safari
We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.
June 2026 newsletter
The June edition of my sponsors-only monthly newsletter is out. If you are a sponsor (or if you start a sponsorship now) you can access it here . This month: Claude Fable 5, GPT-5.6, and US export restrictions GLM-5.2 is the new best open weights model Tokenmaxxing is so over Datasette Apps sqlite-utils and shot-scraper and Datasette Miscellaneous WASM projects Other model releases What I'm using Here's a copy of the May newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy! Tags: newsletter
Google DeepMind and A24 announce first-of-its-kind research partnership
Anthropic wants to develop its own drugs
At the event "The Briefing: AI for Science" earlier this week, Anthropic announced Claude Science, a new "AI workbench for scientists" that pulls fragmented tools and datasets into one environment, and generates figures and visuals. Anthropic, already dominating the industry with its popular coding tools and powerful AI models, framed the launch around what it says is AI's potential to "dramatically accelerate the pace of scientific discovery and the development of healthcare interventions," and touted a long list of biotech and pharma customers already using Claude. Anthropic also went a step further, saying it would develop drugs of its … Read the full story at The Verge.
A behind-the-scenes look at Midjourney’s medical scanner leaves many questions unanswered
A scan of an imaging phantom, segmented to validate how cleanly structures separate under controlled conditions. | Image: Midjourney Medical Midjourney has shown more of its futuristic medical scanner. It still hasn't shown much proof it works. The AI startup, best known for generating images, released a behind-the-scenes video of its dunk-tank ultrasound scanner, which it plans to deploy in spas and hopes will transform medicine with cheap, detailed, radiation-free imaging. The nearly 20-minute tour comes from tech YouTuber Marcin Plaza, who also happens to be an engineer at the company. Plaza frankly describes the scanner as scores of ultrasound probes "hacked apart and slapped on a glorified hot tub with an elevator in it," connected to off-the-shelf computers and Raspberry … Read the full story at The Verge.
AIEWF Daily Dispatch: The great loops debate and the state of AI engineering
The AI Engineer World’s Fair ended with a debate about loops, a report on the state of AI engineering, and closing keynotes focused on what to build next.
Vercel's Andrew Qu on why agents are a new kind of software
The Vercel Chief of Software explains how its agent framework, eve, was created — and why skills, sandboxes and agent-readable websites now matter.