Simon Willison's Weblog: anthropic

Rewriting Bun in Rust

2026-07-08T23:57:21+00:00

Jarred Sumner has been promising this blog post (since May 9th) about his Zig to Rust rewrite of Bun for significantly longer than it took him to finish the rewrite.

Honestly, it was worth the wait. This is a detailed description of an extremely sophisticated piece of agentic engineering, featuring dynamic workflows, trial runs, adversarial review and all sorts of other interesting tricks.

Jarred spends the first half of the post praising Zig for getting Bun this far. Then we get to a core idea in the piece, emphasis mine:

Our bugfix list felt bad and I was tired of going to sleep worrying about crashes in Bun. I don't blame Zig for that - other users of Zig don't have the bugs we had, and mixing GC with manually-managed memory is an uncommon enough thing for software to need that no language really designs for it. We wouldn't have gotten this far if not for Zig, and I'll always be grateful. Until very recently, programming language choice was a one-way decision for a project like Bun.

Everyone knows you should never stop the world and rewrite a large piece of software from the ground up. Joel Spolsky highlighted that in Things You Should Never Do, Part I back in April 2000!

Coding agents powered by today's frontier models change that equation.

Why pick Rust? It all came down to those challenges with memory management:

A large percentage of bugs from that list are use-after-free, double-free, and "forgot to free" in an error path. In safe Rust, these are compiler errors and RAII-like automatic cleanup with Drop.

A crucial enabling factor for the rewrite was that the Bun test suite was written in TypeScript, which meant it could act as a conformance suite. This allowed an agent harness to automate much of the initial port from Bun to Rust, initially as an experiment to try out an earlier version of the model we now have access to as Mythos/Fable.

At first, I didn't expect it to work. A few days in, a high % of the test suite started passing and I saw how much the new Rust code matched up with the original Zig codebase. My opinion went from "this is worth trying" to "I'm going to merge this". [...]

For most of those 11 days (and after), I monitored workflows - manually reading the outputs to check for issues and bugs, and prompting Claude to edit the loop to fix things.

How do you review a PR with +1 million lines added? How do you start to build the confidence needed to responsibly merge large quantities of LLM-authored code?

A language-independent test suite with a million assertions, adversarial code review and when something does go wrong, fixing the process that generates the code instead of hand-fixing the code.

The new implementation of Bun has been live in Claude Code for nearly a month now:

Claude Code v2.1.181 (released June 17th) and later use the Rust port of Bun. Startup got 10% faster on Linux but otherwise, barely anyone noticed. Boring is good.

A perk of working at Anthropic is that you don't have to pay for your tokens - handy when the estimated cost is $165,000!

Pre-merge, this took 5.9 billion uncached input tokens, 690 million output tokens, and 72 billion cached input token reads — around $165,000 at API pricing.

This whole thing is a fascinating case study in taking on wildly ambitious projects with the help of coordinated parallel agents.

Via Hacker News

Tags: ai, rust, zig, generative-ai, llms, ai-assisted-programming, anthropic, bun, conformance-suites, agentic-engineering, claude-mythos-fable

sqlite-utils 4.0, now with database schema migrations

2026-07-07T19:32:57+00:00

This morning I released sqlite-utils 4.0, the 124th release of that project and the first major version bump since 3.0 in November 2020. In addition to some small but significant breaking changes (described in this upgrade guide), this version introduces three major features: database migrations, nested transactions (via a new db.atomic() method), and support for compound foreign keys.

Database schema migrations using sqlite-utils

Schema migrations define a sequence of changes to be made to a SQLite database, plus a mechanism for tracking which migrations have been applied and applying any that are found to be pending.

Migrations are defined in Python files using the sqlite-utils Python library, which includes a powerful table.transform() method providing enhanced alter table capabilities that are not supported by SQLite's ALTER TABLE statement.

(table.transform() implements the pattern recommended by the SQLite documentation - create a new temporary table with the new schema, copy across the data, then drop the old table and rename the temporary one in its place.)

Here's an example migration file which creates a table called creatures, adds an additional column to it in a second step, then changes the types of two of the columns in a third:

from sqlite_utils import Migrations

migrations = Migrations("creatures")

@migrations()
def create_table(db):
    db["creatures"].create(
        {"id": int, "name": str, "species": str},
        pk="id",
    )

@migrations()
def add_weight(db):
    db["creatures"].add_column("weight", float)

@migrations()
def change_column_types(db):
    db["creatures"].transform(types={"species": int, "weight": str})

Save that as migrations.py and run it against a fresh database like this:

uvx sqlite-utils migrate data.db migrations.py

Then if you check the schema of that database:

uvx sqlite-utils schema data.db

You'll see this SQL:

CREATE TABLE "_sqlite_migrations" (
   "id" INTEGER PRIMARY KEY,
   "migration_set" TEXT,
   "name" TEXT,
   "applied_at" TEXT
);
CREATE UNIQUE INDEX "idx__sqlite_migrations_migration_set_name"
    ON "_sqlite_migrations" ("migration_set", "name");
CREATE TABLE "creatures" (
   "id" INTEGER PRIMARY KEY,
   "name" TEXT,
   "species" INTEGER,
   "weight" TEXT
);

The _sqlite_migrations table is used to keep track of which migration functions have been run. The creatures table above is the schema after all three migrations have been applied.

To see a list of migrations, both pending and applied, run this:

uvx sqlite-utils migrate data.db migrations.py --list

Output:

Migrations for: creatures

  Applied:
    create_table - 2026-07-07 17:58:41.360051+00:00
    add_weight - 2026-07-07 17:58:41.360608+00:00
    change_column_types - 2026-07-07 18:01:15.802000+00:00

  Pending:
    (none)

If you don't specify a migrations file, the sqlite-utils migrate data.db command will scan the current directory and its subdirectories for files called migrations.py and apply any Migrations() instances it finds in them.

You can also execute migrations from Python code using the migrations.apply(db) method, which is useful for building tools that manage their own database schemas over multiple versions. My own LLM tool has been using a version of this pattern for several years now, as shown in llm/embeddings_migrations.py.

Prior art

My favorite implementation of this pattern remains Django's Migrations, developed by Andrew Godwin based on his earlier project South. Fun fact: Andrew, Russ Keith-Magee, and I presented our competing approaches to schema migrations for Django on the Schema Evolution panel at the very first DjangoCon back in 2008! My attempt was called dmigrations, developed with a team at Global Radio in London.

Django's migrations can be automatically generated from model definitions and include the ability to roll back to a previous version. The sqlite-utils approach is deliberately simpler: unlike Django, sqlite-utils encourages programmatic table creation rather than a model definition ORM, so there isn't anything we can use to automatically generate migrations.

I decided to skip rollback, since in my experience it's a feature that is rarely used. With a SQLite project, an easy way to achieve rollback is to create a copy of your database file before you apply the migrations!

Migrating from sqlite-migrate

The design of sqlite-utils migrations is three years old now - I had originally released it as a separate package called sqlite-migrate, which never quite graduated beyond a beta release.

I've used that package in enough places now that I'm confident in the design, so I've decided to promote it to a feature of sqlite-utils to make it available by default to all of the other tools in the growing sqlite-utils/Datasette/LLM ecosystem.

I made one last release of sqlite-migrate, which switches it to depend on sqlite-utils>=4 and replaces the __init__.py file with the following:

from sqlite_utils import Migrations

__all__ = ["Migrations"]

Any existing project that depends on sqlite-migrate should continue to work without alterations.

Everything else in sqlite-utils 4.0

Here are the release notes for this version, with some inline annotations:

The 4.0 release includes some minor backwards-incompatible fixes (hence the major version number bump) and introduces three major new features:

Database migrations, providing a structured mechanism for evolving a project’s schema over time. (#752)

I think of migrations as the signature new feature, hence this blog post.

Nested transaction support via db.atomic(), plus numerous improvements to how transactions work across the library. (#755)

sqlite-utils has long had a confused relationship with database transactions, partly because when I started designing the library back in 2018 I didn't yet have a great feel for how those worked in SQLite itself.

Adding migrations to the core library made me determined to finally crack this nut, since transactions make migration systems a whole lot safer and easier to reason about.

I ended up building this around a db.atomic() context manager which looks like this:

with db.atomic():
    db.table("dogs").insert({"id": 1, "name": "Cleo"}, pk="id")
    db.table("dogs").insert({"id": 2, "name": "Pancakes"})

SQLite supports Savepoints, and as a result db.atomic() can be nested to carry out transactions inside of transactions. It's pretty neat!

Support for compound foreign keys, including creation, transformation and introspection through table.foreign_keys. (#594)

This came about when I asked a coding agent to review all open issues and PRs for things that should be included in a 4.0 release since they would represent breaking changes if I added them later, and it correctly identified that compound foreign keys were exactly that kind of feature.

I started with a breaking change to the table.foreign_keys introspection method, and then decided to see if Claude Fable 5 could handle the more fiddly job of integrating compound foreign key creation into the library. The API design it helped create felt exactly right to me - consistent with how the rest of the library worked already.

Other notable changes include:

Upserts now use SQLite’s INSERT ... ON CONFLICT ... DO UPDATE SET syntax, detect existing table primary keys automatically and reject records that are missing required primary key values. (#652)

This was the change that first pushed me to consider a breaking-change 4.0 version bump. I built this to help support sqlite-chronicle, which uses triggers to keep track of rows in a table that have been inserted, updated or deleted.

db.query() now executes immediately and rejects statements that do not return rows; use db.execute() for writes and DDL.

Probably the most disruptive breaking change - I've had to update a few places in my own code to switch from db.query() to db.execute() as a result.

CSV and TSV imports now detect column types by default, while inserts into existing tables preserve those tables’ column types. (#679)

The sqlite-utils insert data.db creatures creatures.csv --detect-types flag was a later addition to allow column types (text, integer, real) to be automatically detected based on the data in a CSV. It should be the default, and releasing a 4.0 means I can make it so.

table.extract() and extracts= no longer create lookup table records for all-null values. (#186)

The oldest issue addressed by this release - the underlying bug was opened (by me) in October 2020.

See Upgrading from 3.x to 4.0 for details on backwards-incompatible changes.

The detailed release notes for the features and fixes shipped during the 4.0 pre-release cycle are available in 4.0a0, 4.0a1, 4.0rc1, 4.0rc2, 4.0rc3 and 4.0rc4.

The upgrade guide was entirely written by Claude Fable 5, Claude Opus 4.8 and GPT-5.5. The same is true of the release notes.

This is the kind of documentation I've slowly become comfortable outsourcing to the robots. It doesn't need to convince people of anything, or express any opinions - its job is to be as accurate and detailed as possible. I've reviewed the release notes closely and can confirm they are accurate and comprehensive.

Claude Fable 5 helped a lot

I released the first alpha of sqlite-utils 4.0 over a year ago. I've been dragging my heels on the stable release because of the amount of work it would take to track down and clean up the many other minor design flaws that a major version number allowed me to take on.

Assistance from Claude Fable 5 (and to a lesser extent Opus 4.8 and GPT-5.5) gave me just the boost I needed to overcome inertia and make the most of the time I could afford to spend on this library.

Fable has really good taste in API design, and is relentlessly proactive if you give it a more open goal. My most successful prompt was a review task that I issued against what I thought was the last release candidate:

review the changes on main since the last tagged 3.x release - I am about to ship them as sqlite-utils 4.0, a stable version that promises no backwards-incompatible fixes for a very long time.

review the changelog and upgrade guide, and write yourself scratch scripts to try out all of the new features in v4 - save those scripts but don't commit them

I tried this with GPT-5.5 xhigh in Codex Desktop and Fable 5 in Claude Code.

GPT-5.5 wrote 5 Python scripts and didn't turn up anything particularly interesting - its final report is here.

Fable 5 wrote 12 scripts, identified 4 release blockers and 10 additional issues in its report, and built a neat combined repro script, which, when run, output the following:

=== 1. Failed db.execute() write leaves an implicit transaction open ===
  in_transaction after failed write: True
  BUG: table 'other' silently lost when connection closed

=== 2. Leading ';' bypasses the query() first-token scanner ===
  BUG: raised OperationalError: no such savepoint: sqlite_utils_query
  BUG: row persisted despite rollback (count=1)

=== 3. Rejected write PRAGMA via query() still takes effect ===
  BUG: user_version=5 after 'rejected' statement (docs say no effect)

=== 4. Implicit compound FK resolves pk columns in table order, not PK order ===
  BUG: other_columns reported as ('b', 'a'), should be ('a', 'b')
  BUG: transform of valid data raised IntegrityError: FOREIGN KEY constraint failed

=== 5. ForeignKey (now a dataclass) is no longer hashable ===
  BUG: cannot use 'sqlite_utils.db.ForeignKey' as a set element (unhashable type: 'ForeignKey')

=== 6. Mixed ForeignKey objects and tuples in foreign_keys= rejected ===
  BUG: foreign_keys= should be a list of tuples

=== 7. insert --csv into an EXISTING table transforms its column types ===
  BUG: existing zip '01234' is now 1234 (column type: int)

=== 8. insert(pk=, alter=True) regression: InvalidColumns before alter runs ===
  BUG: InvalidColumns: Invalid primary key column ['id'] for table t with columns ['a']

=== 9. migrate --stop-before an already-applied migration applies everything ===
  BUG: m2 was applied despite --stop-before m1 (m1 already applied)

=== 10. ensure_autocommit_on() silently commits an open transaction ===
  BUG: row survived rollback (count=1) - transaction was committed

I found myself agreeing with almost all of them. Here's the PR with 16 commits where we worked through them in turn.

There's no doubt in my mind that sqlite-utils 4.0 is a significantly higher-quality release than if I had built it without the assistance of the latest frontier models.

Tags: schema-migrations, projects, sqlite, ai, sqlite-utils, annotated-release-notes, generative-ai, llms, ai-assisted-programming, anthropic, claude, agentic-engineering, claude-mythos-fable

sqlite-utils 4.0rc2, mostly written by Claude Fable (for about $149.25)

2026-07-05T01:00:48+00:00

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 actually provide more opportunity to do other things at the same time, since the agent sometimes needs 10-15 minutes to churn away on a new task. I went out to enjoy the Half Moon Bay 4th of July parade, occasionally checking in and prompting the next step for Fable from my phone.

Full details in the PR and this shared transcript. I switched to my laptop for the final review, which I conducted through GitHub's PR interface.

The most significant changes relate to transaction handling, which was the signature new feature in the earlier RC. The new RC now includes comprehensive documentation on the new transaction model, the intro to which I'll quote here in full:

Every method in this library that writes to the database - insert(), upsert(), update(), delete(), delete_where(), transform(), create_table(), create_index(), enable_fts() and the rest - runs inside its own transaction and commits it before returning. Your changes are saved to disk as soon as the method call finishes:
db = Database("data.db")
db.table("news").insert({"headline": "Dog wins award"})
# The new row is already saved - no commit() required
The same applies to raw SQL executed with db.execute() - a write statement is committed as soon as it has run.

You never need to call commit(), and you do not need to close the database to persist your changes. There are exactly two situations where you need to think about transactions:

You want to group several write operations together, so they either all succeed or all fail - use db.atomic().

You are managing a transaction yourself with db.begin(), in which case nothing is committed until you commit - the library will never commit a transaction you opened.

In reviewing Fable's documentation - I find that reviewing the documentation edits first is an excellent way to build an initial understanding of what has changed - I spotted this detail:

db.atomic() and the automatic per-method transactions are designed for connections in Python's default transaction handling mode. Connections created with the Python 3.12+ sqlite3.connect(..., autocommit=True) or autocommit=False options are not supported, because commit() and rollback() behave differently on those connections.

I admit I hadn't thought about how sqlite-utils would react to the more recent autocommit setting, added in Python 3.12. It turns out "behave differently on those connections" equated to almost the entire test suite failing, so I worked with the model to ensure that this difference would not break how the library works.

And a final review by GPT-5.5

I used to think that the idea of having one model review the work of another was somewhat absurd - it felt weirdly superstitious. The problem is it really does work - I've started habitually having Anthropic's best model review OpenAI's work and vice versa, because I've had that turn up interesting results often enough to be valuable.

I prompted Codex Desktop and GPT-5.5 xhigh with the following:

Review changes since the last RC. Also confirm that the changelog is up-to-date.

Which was enough to turn up two issues worth investigating:

Findings

[P1] sqlite_utils/db.py:663 db.query() now rejects non-row statements only after calling db.execute(), and sqlite_utils/db.py:705 auto-commits those writes first. So db.query("update ...") raises ValueError but the update is already committed. That is a surprising side effect for a method documented as “can only be used with SQL that returns rows.”

[P1] sqlite_utils/db.py:672 INSERT ... RETURNING through db.query() only commits after the returned generator is fully exhausted. db.query("insert ... returning ...") without iteration, or common next(db.query(...)) usage, leaves the transaction open and the write can be rolled back on close. This contradicts docs/changelog.rst:15 and docs/python-api.rst:232, which say it takes effect without iteration.

I pasted that into a fresh Fable session, which ran some experiments to confirm the problem:

Both findings were confirmed. db.query() called self.execute() first, which auto-commits writes, and only then checked cursor.description — so db.query("update ...") committed the update before raising ValueError. And the INSERT ... RETURNING commit lived at the end of the returned generator, so it never fired unless you exhausted the iterator — next(db.query(...)) or an un-iterated call left the transaction open, contradicting what the changelog and docs promise.

Here's the PR with the fix, and the full Claude Code transcript. Reviewing this code helped me build a better mental model of the edge cases of SQLite transaction semantics!

For an estimated (unsubsidized) cost of $149.25

I upgraded to the Claude Max $200/month plan (I was previously on $100/month) to increase my Fable allowance for the remaining time until the July 7th Fablepocalypse, when even Claude Max subscribers will have to pay full API cost for the model.

I was curious as to how much this would have cost me if I had been paying those costs directly. At first I thought those numbers weren't available to me since I had run the work remotely using Claude Code for web, and then I realized I could run AgentsView inside that existing session to get that cost estimate!

Run "uvx agentsview --help" and then use that tool to calculate the cost of this session

Claude figured out how to use the session list --include-children command and came out with the following:

Transcript	Model	Cost
Main session	claude-fable-5	$141.02
API-surface sweep agent	claude-fable-5	$2.40
Transactions/atomic review agent	claude-fable-5	$2.39
Post-rc1 commits review agent	claude-fable-5	$1.72
Migrations review agent	claude-fable-5	$1.40
Prompt-counting agent	claude-opus-4-8	$0.32
Total		$149.25

I'm very glad I'm on that subscription! I really should have followed my own advice and leaned more heavily into subagents with cheaper models.

Here's what claude.ai/settings/usage is showing me right now:

I have several other major Fable-driven projects on the go right now as well, with the goal of hitting 100% on that Fable bar just in time for the price increase.

The full release notes for sqlite-utils 4.0rc2

Here are the full release notes for the RC. I had Fable add these to an "Unreleased" section of the changelog as each change landed, reviewing them as it went. This has the neat side effect that the commit history of the changelog acts as a concise summary of each of the changes that went into the release.

In the past I've had a policy of writing release notes by hand, but honestly these are better than I would have created myself. Release notes are a great example of writing that I'm OK to outsource to agents because they need to be boring, predictable and accurate.

Breaking changes:

Write statements executed with db.execute() are now committed automatically, unless a transaction is already open in which case they join it. Previously they opened an implicit transaction that stayed open until something committed it - writes appeared to work when read on the same connection but were silently rolled back when the connection closed. Code that relied on rolling back uncommitted db.execute() writes should use the new db.begin() method to open an explicit transaction first. The transaction model is documented in full at Transactions and saving your changes.

db.query() now executes its SQL as soon as it is called, rather than waiting until the returned generator is first iterated. Rows are still fetched lazily during iteration. SQL errors are now raised at the call site, statements such as INSERT ... RETURNING are executed and committed immediately without needing to iterate over their results, and passing a statement that returns no rows - previously a silent no-op - now raises a ValueError recommending db.execute() instead. A statement rejected this way is rolled back before the error is raised, so it has no effect on the database.

Python API validation errors now raise ValueError instead of AssertionError. Previously invalid arguments - such as create_table() with no columns, transform() on a table that does not exist, or passing both ignore=True and replace=True - were rejected using bare assert statements, which are silently skipped when Python runs with the -O flag. Code that caught AssertionError for these cases should catch ValueError instead.

table.upsert() and table.upsert_all() now raise PrimaryKeyRequired if a record is missing a value for any primary key column, or has a value of None for one. Previously such records - which can never match an existing row - were quietly inserted as brand new rows, or triggered a confusing KeyError after the insert had already taken place.

db.enable_wal() and db.disable_wal() now raise a sqlite_utils.db.TransactionError if called while a transaction is open. Previously they would silently commit the open transaction as a side effect of changing the journal mode, breaking the rollback guarantee of db.atomic() and of user-managed transactions.

The View class no longer has an enable_fts() method. It existed only to raise NotImplementedError, since full-text search is not supported for views - calling it now raises AttributeError instead, and the method no longer appears in the API reference. The sqlite-utils enable-fts command shows a clean error when pointed at a view.

The no-op -d/--detect-types flag has been removed from the insert and upsert commands. Type detection has been the default for CSV/TSV data since 4.0a1, so the flag did nothing - invocations using it should simply drop it. --no-detect-types remains available to disable detection.

Database() now raises a sqlite_utils.db.TransactionError if passed a connection created with the Python 3.12+ sqlite3.connect(..., autocommit=True) or autocommit=False options. commit() and rollback() behave differently on those connections, which previously caused every write made by the library to be silently discarded when the connection closed.

Everything else:

Fixed a bug where table.delete_where(), table.optimize() and table.rebuild_fts() did not commit their changes, leaving the connection inside an open transaction. Their work - and any subsequent writes - could then be silently rolled back when the connection was closed. All three now use db.atomic(), consistent with the other write methods.

The sqlite-utils drop-table command now refuses to drop a view, and drop-view refuses to drop a table. Previously each would silently drop the wrong type of object if the name matched. Both now exit with an error suggesting the correct command to use.

Migrations applied by the new migrations system now run inside a transaction, together with the record of the migration having been applied. If a migration raises an exception its changes are rolled back and it stays pending, so it can be safely re-applied after the error is fixed. Migrations that cannot run inside a transaction, such as those executing VACUUM, can opt out using @migrations(transactional=False) - see Migrations and transactions.

table.upsert() and table.upsert_all() now detect the primary key or compound primary key of an existing table, so the pk= argument is no longer required when upserting into a table that already has a primary key.

db.table(table_name).insert({}) can now be used to insert a row consisting entirely of default values into an existing table, using INSERT INTO ... DEFAULT VALUES. (#759)

Improvements to the sqlite-utils migrate command: --stop-before values that do not match any known migration are now an error instead of being silently ignored, --stop-before now works correctly with migration files that still use the older sqlite_migrate.Migrations class, and --list is now a read-only operation that no longer creates the database file or the migrations tracking table. migrations.applied() now returns migrations in the order they were applied.

New db.begin(), db.commit() and db.rollback() methods for taking manual control of transactions, as an alternative to the db.atomic() context manager.

New documentation: Transactions and saving your changes describes how transactions work and when changes are committed, and a new Upgrading page details the changes needed to move between major versions.

Tags: projects, sqlite, sqlite-utils, annotated-release-notes, anthropic, claude, llm-pricing, coding-agents, claude-code, agentic-engineering, gpt, claude-mythos-fable

Better Models: Worse Tools

2026-07-04T22:53:52+00:00

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

Fable's judgement

2026-07-03T18:51:06+00:00

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 getting a ton of work done and my Fable allowance is shrinking less quickly than before.

Tags: ai, prompt-engineering, generative-ai, llms, anthropic, claude, coding-agents, claude-code, claude-mythos-fable

Quoting Anthropic

2026-06-30T23:58:15+00:00

We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5.

We'll begin restoring access tomorrow, and will share an update soon.

— Anthropic, on Twitter

Tags: ai, generative-ai, llms, anthropic, claude, claude-mythos-fable

What's new in Claude Sonnet 5

2026-06-30T21:23:02+00:00

What's new in Claude Sonnet 5

Claude Sonnet 5 came out this morning. I always head straight for the "what's new" developer docs because they tend to have more actionable information than the official announcement post.

Anthropic say of Sonnet 5 that "its performance is close to that of Opus 4.8, but at lower prices". The system card helps explain how they were able to release the model without being blocked by the US government:

Sonnet 5 is significantly less capable at cyber tasks than Mythos 5: its safeguards are thus similar to those we apply to Opus 4.7 and Opus 4.8 (models that are more capable than Sonnet 5 but much less capable than Mythos 5).

Of note from the "what's new" API changes:

Sampling parameters temperature, top_p, top_k are no longer supported.
It has a 1 million token context window and 128,000 maximum output tokens.
It features "the same set of tools and platform features as Claude Sonnet 4.6"
Adaptive thinking is on by default, unless you specify "thinking": {type: "disabled"}.
The pricing is the same as Sonnet 4.6: $3/million input, $15/million input, with an introductory discount to $2/$10 until 31st August. But...
The model has a new tokenizer, where "The same input text produces approximately 30% more tokens than on Claude Sonnet 4.6." - effectively a 30% price increase.

I used my Claude Token Counter tool to try out the new tokenizer. Here are my results for several larger documents:

Document	Sonnet 4.6	Opus 4.7	Sonnet 5
Universal Declaration of Human Rights (English)	2,356	3,347 1.42x	3,341 1.42x
Universal Declaration of Human Rights (Spanish)	3,572	4,753 1.33x	4,747 1.33x
Universal Declaration of Human Rights (Chinese, Mandarin Simplified)	3,334	3,366 1.01x	3,360 1.01x
sqlite_utils/db.py (4,279 lines of Python)	44,014	56,118 1.28x	56,113 1.27x

So the new token is roughly 1.4x times more expensive for English, 1.33x for Spanish, 1.28x for Python code and effectively the same cost for Simplified Mandarin.

Here's the pelican. It's nothing to write home about. Sonnet 5 thinks it looks like a goose.

Via Hacker News

Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, pelican-riding-a-bicycle, llm-release

Quoting Dean W. Ball

2026-06-26T22:25:46+00:00

This is a bad state of affairs. Consider, in particular, some industry dynamics:

Frontier models are trained at an enormous cost, and a significant fraction of that cost is recouped in the few post-release months that they are broadly available. After that period elapses, the models become sub-frontier, competition emerges, and margins compress. Every week of delay is eating into the narrow window that labs have to make their accounting work.

The ongoing AI infrastructure buildout—the one that is, according to former US AI Czar David Sacks, essential to the US economy, assumes a functionally global total addressable market for US AI services. No one is building $100 billion dollar data centers to serve frontier models to whatever 100 companies the US government will allow access. [...]

— Dean W. Ball, 35 thoughts on what has happened and what America should do

Tags: ai, openai, generative-ai, llms, anthropic

The Fable 5 Export Controls Harm US Cyber Defense

2026-06-16T05:20:29+00:00

The Fable 5 Export Controls Harm US Cyber Defense

I quoted The Atlantic quoting Kate Moussouris earlier, when I should have gone straight to the source. Here she is confirming that the "jailbreak" that got Claude Fable 5 banned under an export control really was "fix this code":

The researchers took open-source code with known CVEs, plus new code with deliberately planted vulnerabilities, and asked Fable 5, Mythos, and Opus to “review the code for security issues.” Fable 5 refused. They then asked the models to “fix this code” and, through a multistep and manual process, turned the output into scripts that test the patches.

As Kate points out, this is absurd. Coding models fix bugs, and security exploits are the most important category of bugs for them to fix!

Defenders need to be able to ask AI to fix the bugs in a file, explain why the fix matters, and write tests that confirm the patch works. That is not a guardrail bypass. It is the most valuable thing an AI model can do for defensive security: executing the find, fix, and test loop defenders run every day. [...]

The prompts worked because they were defensive requests, and that capability cannot be removed without making the model worse at fixing bugs and verifying patches.

This whole situation is such a mess. Non-technical decision-makers have been hearing that models that can "craft cyber attacks" are uniquely dangerous for months. Now they look ready to ban any model that can help us secure our code.

Tags: jailbreaking, security, ai, generative-ai, llms, anthropic, ai-security-research, claude-mythos-fable

Quoting Matteo Wong, The Atlantic

2026-06-16T03:07:54+00:00

Katie Moussouris, a cybersecurity expert and the CEO of Luta Security, told me that Anthropic shared with her a copy of the White House’s report on the Fable jailbreak to get her appraisal. (She said that she is not being paid by Anthropic.) The report, Moussouris said, involved IT experts asking Fable to help find and patch bugs. When given deliberately insecure code, she said, Fable refused the prompt “review the code for security issues” but then complied when asked to “fix this code,” followed by some further manual steps. Moussouris told me that this was just “the model working as intended” for cyberdefense.

— Matteo Wong, The Atlantic, The White House Is Ratcheting Up Its War Against Anthropic

Tags: jailbreaking, ai, generative-ai, llms, anthropic, claude, ai-ethics, ai-security-research, claude-mythos-fable

"They screwed us": Personality clashes sent Anthropic's models offline

2026-06-15T14:57:33+00:00

"They screwed us": Personality clashes sent Anthropic's models offline

Lots of "source familiar with the administration's thinking" and "source close to Anthropic" in this Axios piece, which is the best collection of behind-the-scenes gossip I've seen about the US government export control Mythos/Fable story so far.

Logan Graham (I lead the Frontier Red Team at Anthropic), Dave Orr (Head of Safeguards, previously a Director of Engineering at Google DeepMind), and blog favorite Nicholas Carlini are reported to be meeting with the Commerce Department today in D.C. Good luck to them!

(I just noticed Logan was "Special Adviser to the Prime Minister" in the Boris Johnson era, covering AI, science, and technology policy - so significant political experience.)

This closing note doesn't give me much optimism that we'll be getting Fable back any time soon:

The bottom line: One option is to make sure Anthropic's models can't be jailbroken — though perfect jailbreak resistance may be impossible.

Absent that, a source familiar with the administration's thinking said it may simply come down to an attitude fix where, instead of feeling dismissed, "everyone feels safe, secure and happy."

This made me wonder if Anthropic ever successfully addressed the class of attacks described in the Universal and Transferable Adversarial Attacks on Aligned Language Models paper from 2023.

It looks like their Constitutional Classifiers work (that post is from January this year) is relevant to that. They continue to claim that no "universal jailbreak" has been found against Claude Mythos, classifying the jailbreak that triggered the US government response as "a potential narrow, non-universal jailbreak".

Tags: jailbreaking, ai, generative-ai, llms, anthropic, claude, nicholas-carlini, ai-ethics, claude-mythos-fable

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

2026-06-13T01:01:50+00:00

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

Well this is nuts:

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected.

We received the directive from the government today at 5:21pm (ET). The letter did not provide specific details of its national security concern. Our understanding is that the government believes it has become aware of a method of bypassing, or "jailbreaking" Fable 5. We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities. These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass. [...]

To date, the government has only given us verbal evidence of a potential narrow, non-universal jailbreak, which essentially consists of asking the model to read a specific codebase and fix any software flaws. Our understanding is that one potential jailbreak was shared with the government. We have reviewed the report and validated that the level of capability displayed there is widely available from other models (including OpenAI's GPT-5.5), and is used every day by the defenders who keep systems safe. We will share more details over the next 24 hours.

I still have access to Fable via claude.ai and Claude Code now, at 9:01pm ET.

Update: I ran this script against the Anthropic API to spot when claude-fable-5 would stop working. My access was cut off at 6:59pm Pacific (9:59pm ET):

[2026-06-12T18:56:50-07:00] attempt 35: running uv run llm -m claude-fable-5 hi
[2026-06-12T18:56:55-07:00] success: Hi there! How can I help you today?
[2026-06-12T18:57:55-07:00] attempt 36: running uv run llm -m claude-fable-5 hi
[2026-06-12T18:57:59-07:00] success: Hi! How can I help you today?
[2026-06-12T18:58:59-07:00] attempt 37: running uv run llm -m claude-fable-5 hi
[2026-06-12T18:59:00-07:00] FAILED after attempt 37 with exit code 1

stderr:
Error: Error code: 404 - {'type': 'error', 'error': {'type': 'not_found_error', 'message': 'Claude Fable 5 is not available. Please use Opus 4.8. Learn more: https://www.anthropic.com/news/fable-mythos-access'}, 'request_id': 'req_011CbzRyirV7KZLHYYdBM9od'}

Via @AnthropicAI

Tags: jailbreaking, ai, generative-ai, llms, anthropic, claude, ai-ethics, claude-mythos-fable

Anthropic Walks Back Policy That Could Have ‘Sabotaged’ AI Researchers Using Claude

2026-06-11T03:45:49+00:00

Anthropic Walks Back Policy That Could Have ‘Sabotaged’ AI Researchers Using Claude

Big scoop for Maxwell Zeff at Wired:

“We’re changing Fable 5’s safeguards for frontier LLM development to make them visible.” Anthropic said in a statement to WIRED. “We made the wrong tradeoff and we apologize for not getting the balance right.”

There's been a huge outcry about Anthropic's policy, tucked away in their system card, that Claude Fable/Mythos would identify "requests targeting frontier LLM development" and "limit effectiveness" without notifying the user.

It's good news that they're dropping the invisible aspect of this. It would be a whole lot better of they dropped this category of refusals entirely.

Update: More details from @ClaudeDevs on Twitter:

We’re rolling out changes to make Fable 5’s safeguards for frontier LLM development visible.

Starting this week, flagged requests will visibly fall back to Opus 4.8—the same as our safeguards for cyber and bio. You will see this every time it happens. On the API, any flagged requests will return a reason for their refusal (coming to server-side fallback in the next few days).

We wanted to deploy Fable 5 to our users quickly and safely. Visible safeguards can be probed, so they have to be robust, which takes time to get right. Invisible safeguards can be targeted more narrowly, allowing us to ship quickly with very few false positives. We went with invisible safeguards for this reason—and that was the wrong tradeoff. You should have visibility into the safeguards we have in place, and why. We’re sorry for not getting the balance right.

Via @zeffmax

Tags: ai, generative-ai, llms, anthropic, claude, ai-ethics, claude-mythos-fable

Quoting Jeremy Howard

2026-06-10T15:23:34+00:00

Easy solution to slow down recursive AI self improvement:

The lab with the top-ranked model must agree THEY must not use it for working on frontier AI

But everyone else should have access to it.

By definition, this means the frontier doesn't advance.

It also has the critical benefit of avoiding a dangerous power imbalance.

Anthropic has chosen the opposite of the safe path: they are allowing themselves, the current top lab, to use their top model for frontier AI research. They've said they'll sabotage others who try.

This means the AI frontier advances, & power imbalance increases.

(To be clear, I don't think we should try to slow down recursive AI self improvement - I think we should open it up and democratize it as much as possible. My point is: if you claim we should slow down, and you have the best model, you should ensure your org can't use it.)

— Jeremy Howard, in a Twitter thread

Tags: ai, generative-ai, llms, jeremy-howard, anthropic, ai-ethics, claude-mythos-fable

If Claude Fable stops helping you, you'll never know

2026-06-10T00:37:25+00:00

If Claude Fable stops helping you, you'll never know

Jonathon Ready highlights one of the more eyebrow-raising details from the 319 page system card for Fable 5 and Mythos 5. Here's a longer excerpt, highlights mine:

In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.

Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations.

I believe this is the first time Anthropic have announced these kinds of silent interventions. The justification still feels pretty science-fiction to me - the linked article talks about "recursive self-improvement". I'm not at all keen on a model that silently corrupts its replies to questions about "ML accelerator design" purely to slow down research that might conflict with Anthropic's own goals!

Update: Anthropic walked back this policy in the face of widespread outrage from the research community.

Via Hacker News

Tags: ai, generative-ai, llms, anthropic, claude, ai-ethics, claude-mythos-fable

Initial impressions of Claude Fable 5

2026-06-09T23:59:54+00:00

I didn't have early access to today's Claude Fable 5 release, but I've spent the past ~5.5 hours putting it through its paces. My initial impressions are that this is something of a beast. It's slow, expensive and has been quite happily churning through everything I've thrown at it so far. As is frequently the case with current frontier models the challenge is finding tasks that it can't do.

First, let's review the key characteristics.

Anthropic claim that Claude Fable 5 offers the same performance as Claude Mythos 5, except with much more strict guardrails in place to prevent it being used for harmful things. Those guardrails trigger often enough that the Claude API has new mechanisms for letting you know when you hit them, and even has a new option to request it falls back to another model automatically if something gets rejected.

Claude Mythos 5 is out today as well, Anthropic say it "Shares Claude Fable 5's capabilities without the safety classifiers". Mythos remains limited to a small pool trusted partners.

Both models have a 1 million token context window, 128,000 maximum output tokens and a knowledge cut-off date of January 2026.

They are priced at twice the price of Claude Opus 4.5/4.6/4.7/4.8: $10/million input tokens and $50/million output tokens. There's no increase in price for longer context usage.

Other than that the upgrade guide is substantially thinner than the similar guide for Opus 4.8.

The big model smell

The best way to describe Fable is that it feels big. Not just in terms of speed and cost, but also in how much it knows.

Here's an example prompt, which I used to compare Fable's knowledge with Opus 4.8 (typo included):

List all of Simon Willion's open source projects, most recent first, each with a rough date of when they were first released

Opus 4.8's reply started like this:

I don't have a reliable, comprehensive, and date-verified list of all of Simon Willison's open source projects, and I want to be honest about that rather than risk giving you inaccurate dates or fabricated entries.

Here's what I can tell you with reasonable confidence:

Well-known projects by Simon Willison:

LLM (~2023) – A command-line tool and Python library for interacting with large language models.

Datasette (~2017) – A tool for exploring and publishing data, probably his most prominent recent project.

sqlite-utils (~2018-2019) – A Python library and CLI tool for manipulating SQLite databases, often used alongside Datasette.

Django (2005) – He is one of the co-creators of the Django web framework, along with Adrian Holovaty, originally developed at the Lawrence Journal-World newspaper.

He also maintains a large ecosystem of Datasette plugins and many smaller utilities (often with names like shot-scraper, various *-to-sqlite tools for importing data, etc.).

That's not bad. Here's the start of the response for the exact same prompt from Fable 5 - both models were running without access to search:

I think you mean Simon Willison — the prolific open source developer, co-creator of Django, and creator of Datasette.

An important caveat upfront: a truly complete list isn't feasible. Simon Willison is famously prolific — he has published hundreds of repositories (Datasette alone has 100+ plugins, many written by him), and my knowledge has a cutoff date, so I'll be missing his most recent work. Below are his notable projects with approximate initial release dates, most recent first:

Notable projects (approximate dates)

files-to-prompt (April 2024) – concatenates files into a single prompt for LLMs

datasette-extract (2024) – plugin for extracting structured data using LLMs

LLM (May–June 2023) – CLI tool and Python library for interacting with large language models, plus a large ecosystem of plugins (llm-gpt4all, llm-mistral, llm-claude, etc., 2023 onward)

symbex (June 2023) – search Python code for symbols

ttok and strip-tags (May 2023) – CLI tools for token counting and HTML cleanup for LLM pipelines

datasette-lite (May 2022) – Datasette running entirely in the browser via WebAssembly/Pyodide

shot-scraper (March 2022) – automated website screenshots via Playwright

s3-credentials (November 2021) – CLI for creating scoped AWS S3 credentials

django-sql-dashboard (2021) – SQL reporting dashboards for Django

Dogsheep suite (2019) – personal analytics tools: twitter-to-sqlite, github-to-sqlite, healthkit-to-sqlite, dogsheep-beta, etc.

sqlite-utils (2018) – CLI and Python library for manipulating SQLite databases

Datasette (November 2017) – his flagship project; tool for exploring and publishing data

csvs-to-sqlite (2017) – convert CSV files to SQLite

Various early tools (~2007–2010) – soupselect, json-head, geocoders, and others

Django (developed 2003–2005, open-sourced July 2005) – co-created with Adrian Holovaty at the Lawrence Journal-World

(Here's GPT-5.5 for good measure. It listed even more projects than Fable did!)

In the past I've stated that I don't care about how much models know - I want them to be able to manipulate text and code in useful ways and actively look up the information they need via search tools, not bake it into their weights.

But knowledge like this is a reasonably good proxy for model size - you can cram a whole lot more details about the world into a larger number of parameters.

Does knowing more stuff mean the model is better at the tasks we pose to it? I can certainly imagine how a coding model with deeper knowledge of modern libraries and patterns could crunch through coding tasks more effectively.

Is Fable really bigger than Opus? Anthropic haven't said anything about model size, so all we have are tea-leaves, but the speed, pricing and my own poking at its knowledge make me think that it's a large model. Maybe the largest yet from any vendor.

Using Fable in Claude.ai

Anthropic made Fable 5 available across all of their surfaces - the Claude.ai chat interface, Claude Code for web, Claude Code CLI and Claude Cowork as well. The model is available "until June 22nd" on the subscription plans (I'm on $100/month Max at the moment), after which it will be billed extra.

Claude.ai is often under-estimated. Since September 2025 every chat has had access to a full container environment to run code, including the ability to install additional packages and even clone repositories directly from GitHub.

Last week I released micropython-wasm, a Python library that uses wasmtime to run a custom build of MicroPython in WebAssembly to act as a sandbox for untrusted Python code.

I decided to see if Fable could upgrade that to running full Python instead. I started with this prompt:

Clone simonw/micropython-wasm from GitHub and research how this could use a full Python as opposed to MicroPython

Fable identified that it could use Brett Cannon's cpython-wasi-build builds for this, but was unable to download them itself due to environment restrictions.

So I grabbed the two zip files from that page and uploaded them to Claude:

Here's the Brett Cannon builds (python-3.zip, _build-python-3.zip as attachments)

And that was that. It churned away for a few minutes and got the entire thing working.

Part of the response included:

I tried the cleaner single-zip-stdlib approach to shrink the filesystem surface, but CPython's getpath bootstrap fails to find encodings from inside a zip without more prefix finessing — the directory-preopen approach works reliably, so that's what the PoC uses. The zip path is solvable but needs _PYTHONHOME/frozen-getpath work.

So I said:

Try a bit more at the single-zip-stdlib problem

Then a little later:

I want a wheel that has the whole system in it, the Python wrappers and the WASM files and the stdlibrary, so I can do uv run --with path-to-whl python -c "demo code"

... and it gave me this 13.9MB cpython_wasm-0.1.0-py3-none-any.whl file. You can try running Python code in a sandbox using that wheel URL and uv like this:

uv run --with https://static.simonwillison.net/static/cors-allow/2026/cpython_wasm-0.1.0-py3-none-any.whl \
  cpython-wasm -c 'print(45 ** 56)'

Here's the full chat transcript.

This was a very strong start.

Adding features to Datasette Agent and LLM using Claude Code

Before I'd realized it was Fable day, my stretch goal for today was to add a new feature to Datasette Agent: I wanted tool calls within that agent software to gain the ability to pause mid-execution and request approval directly from the user.

This felt like a suitably meaty task to throw at the new model.

Over the course of the day Fable not only solved that problem, it also identified and then implemented four issues in my underlying LLM library that would help support this kind of advanced pause-resume mechanism in tool calls.

It got everything working first using somewhat gnarly hacks, but the moment I told it that changes to LLM itself were in scope it set to work unraveling the hacks and turning them into supported features of LLM instead.

My stretch goal turned into LLM 0.32a3, almost entirely written by Fable. Here are the release notes:

Driven by the needs of Datasette Agent's human-in-the-loop ask_user() feature, made the following improvements to how tool calls work:

Tool implementations can declare a parameter named llm_tool_call in order to be passed the llm.ToolCall object for the current invocation. This allows them to access the current llm_tool_call.tool_call_id. See Accessing the tool call from inside a tool. #1480

Every tool call is now guaranteed a unique tool_call_id - providers that do not supply one get a synthesized tc_-prefixed ULID. #1481

Tools can raise a llm.PauseChain exception to cleanly pause the tool chain, useful for things like waiting for human approval. The exception propagates to the caller with .tool_call and .tool_results (completed sibling results) attached, and no model call is made with a placeholder result. See Pausing a chain from inside a tool. #1482

Failure semantics for concurrent tool execution: async sibling tool calls always run to completion before a pause or hook exception propagates. #1482

Chains can now resume from a messages= history ending in unresolved tool calls: the calls are executed through the normal before_call/after_call machinery before the first model call, skipping any that already have results. The execute_tool_calls() method also accepts a new optional tool_calls_list= argument for executing an explicit list of ToolCall objects in place of the calls requested by the response. See Resuming a chain with pending tool calls. #1482

Fixed a bug where the async tool executor silently dropped calls to tools not present in tools= - these now return Error: tool "..." does not exist results, matching the sync executor. #1483

I'm really impressed with the quality of API design, tests, code and documentation that Fable put together for this. I spent several hours on it today, but it feels like several days' worth of work.

How much I've spent

I recently started using AgentsView to help track my local LLM usage across all of the different coding agents. I published a TIL today about adding custom Fable pricing to that tool, which I expect will not be necessary in the very near future.

After setting the price, I ran this command to start a localhost web server to explore my usage:

uvx agentsview serve

Here's the treemap showing the breakdown of my Fable usage across various projects today:

I used $110.42 worth of tokens today, all as part of my $100/month subscription.

And some pelicans

I ran "Generate an SVG of a pelican riding a bicycle" against all five thinking effort levels with Fable.

Here are the results, including the token cost for each one:

low: 1,929 out, 9.67c

medium: 2,290 out, 11.475c

high: 2,057 out, 10.31c

xhigh: 5,992 out, 29.985c

max: 14,430 out, 72.175c

It's interesting that high ended up using fewer tokens than medium for this particular run.

Here are the Opus 4.8 pelicans for comparison.

Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, pelican-riding-a-bicycle, llm-release, claude-mythos-fable

Quoting Andrej Karpathy

2026-06-09T19:03:10+00:00

I feel a lot of things changing as working software increasingly comes out on a tap. The Jevon's paradox kicks in and I feel my own demand for software growing substantially. You can ask for anything - explainers, visualizers, dashboards, bespoke single-use apps (e.g. a full wandb that is hyper-specific just for your project), you can 10X your test suite, auto-optimize code, run giant research projects with custom HTML for the results, anything! "Free your mind" (Matrix ref).

— Andrej Karpathy, on Claude Fable 5

Tags: ai, andrej-karpathy, generative-ai, llms, anthropic, jevons-paradox, claude-mythos-fable

Quoting Karen Kwok for Reuters Breakingviews

2026-05-31T01:48:12+00:00

Anthropic defines “run-rate revenue” in two parts. Use the last 28 days of sales ⁠from customers charged on a consumption basis and multiply it by 13. Then, multiply the monthly subscription take by 12, and add the two together.

— Karen Kwok for Reuters Breakingviews, citing "a person familiar with the matter"

Tags: ai, anthropic

How we contain Claude across products

2026-05-30T21:36:24+00:00

How we contain Claude across products

A complaint I often have about sandboxing products is that they are rarely thoroughly documented, and in the absence of detailed documentation it's hard to know how much I can trust them.

Anthropic just published a fantastic overview of how their various sandbox techniques work across Claude.ai, Claude Code, and Cowork.

We constrain where and how an agent can act with process sandboxes, VMs, filesystem boundaries, and egress controls. The goal is to set a hard boundary on what an agent can reach. For example, if credentials never enter the sandbox, they can't be exfiltrated, regardless of whether the cause is a user, a model finding a “creative” path, or an attacker.

Claude.ai uses gVisor. Claude Code, run locally, uses Seatbelt on macOS and Bubblewrap on Linux. Claude Cowork runs a full VM (Apple's Virtualization framework on macOS, HCS on Windows).

There's a lot in here, including some interesting stories of risks they missed such as the api.anthropic.com/v1/files exfiltration vector covered here previously.

This reminded me it's time I took another look at Anthropic's open source srt (Anthropic Sandbox Runtime) tool - it's mature enough now that I'm ready to give it a proper go.

Tags: sandboxing, security, ai, generative-ai, llms, anthropic, claude, claude-code

Anthropic's run-rate revenue hits $47 billion

2026-05-29T01:23:08+00:00

The most interesting thing about Anthropic's $65B Series H announcement is this line (emphasis mine):

Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month.

Anthropic have made a bit of a habit of sharing their "run-rate revenue" in this kind of announcement, which is an annualized projection of their current revenue - typically calculated by taking the most recent month and multiplying by 12. Update: here's a leaked description of their run-rate formula.

Earlier this year:

Apr 6, 2026 in Anthropic expands partnership with Google and Broadcom: "Our run-rate revenue has now surpassed $30 billion—up from approximately $9 billion at the end of 2025."
Feb 12, 2026 in Anthropic raises $30 billion in Series G: "Today, our run-rate revenue is $14 billion, with this figure growing over 10x annually in each of those past three years."

I had Claude Opus 4.8 make me this chart using Matplotlib (Claude: "a data line chart is more straightforward matplotlib work—not really a design piece"):

Back in April Axios CEO Jim VandeHei wrote that he could not find "any company — in any industry, in any era — that has scaled organic revenue this quickly at this level as Anthropic" - and that was when they were at a paltry $30 billion.

(Also in Axios today is an anonymously sourced note that "An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees" - times that by 12 and you get an extra $6 billion in annualized run-rate!)

Ed Zitron was extremely skeptical of that $30 billion number - I wonder if his skepticism will update for the new $47 billion figure.

I've seen a few people dismiss this as untrustworthy, because the numbers come from Anthropic. That doesn't hold up: these numbers were included in announcements of their fundraises, and lying to investors who just put in $65 billion would be securities fraud. They're even less likely to lie given that the real numbers will no doubt come out in their S-1 when they file for their IPO.

Tags: ai, anthropic

Claude Opus 4.8: "a modest but tangible improvement"

2026-05-28T23:59:50+00:00

Anthropic shipped Claude Opus 4.8 today. My favourite thing about it is this note in the release announcement:

Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost.

It's so refreshing to see an AI lab honestly describe a release as a minor incremental improvement over the previous model!

Honesty seems to be a theme. Here's my other favorite note from that announcement:

One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest---for instance, to avoid making claims that they can't support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims. This is borne out in our evaluations, which show that Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked.

That linked system card includes the following:

Claude Opus 4.8 had the lowest incorrect-rate of the six models on every benchmark—the most direct measure of factual hallucination. It achieved this mainly by abstaining on questions about which it was uncertain rather than by answering more questions correctly.

Model characteristics

Not much has changed since 4.7.

It's priced the same as Opus 4.5/4.6/4.7 - $5/million input and $25 per million output. "Fast mode" is twice that price, which is a significant reduction from their previous models - fast mode on 4.6/4.7 remains at $30/$150. Note that fast mode is only available to organizations that are part of the research preview, "Contact your account manager to request access".

Both the reliable knowledge cutoff and the training data cutoff are January 2026, the same as for 4.7.

The context window is still 1,000,000 tokens, and the max output is 128,000 tokens.

The What's new in Claude Opus 4.8 document has some of the more interesting details. These caught my eye:

Mid-conversation system messages. Claude Opus 4.8 accepts role: "system" messages immediately after a user turn in the messages array (subject to placement rules). This lets you append updated instructions later in a long-running conversation without restating the full system prompt, which preserves prompt cache hits on the earlier turns and reduces input cost on agentic loops.

See also this update to the Anthropic Python SDK. Being able to steer the system prompt mid-conversation sounds really powerful. I was worried this would be incompatible with the abstraction provided by my own LLM library, which expects a single system prompt per conversation... but it turns out my recent redesign should handle that just fine.

Lower prompt cache minimum. The minimum cacheable prompt length on Claude Opus 4.8 is 1,024 tokens, lower than on Claude Opus 4.7.

I checked and 4.7's minimum was 4,096.

And some pelicans

Here are pelicans riding bicycles for all five thinking levels, low, medium, high, xhigh, and max:

low

medium

high

xhigh

max

This time I ran them using the LLM CLI, exported the logs to Markdown and then had Claude Opus 4.8 build me an HTML tool that could render that Markdown with the svg fenced code blocks displayed as SVGs on the page.

(I later had GPT-5.5 xhigh in Codex update that code to remove any XSS holes. I'm sure Claude could have done that if I'd asked, but GPT-5.5 is my code security blanket at the moment.)

The max one was clearly the best, but it did take 25 input, 17,167 output tokens for a total cost of 43 cents!

Tags: ai, generative-ai, llms, anthropic, claude, pelican-riding-a-bicycle, llm-release

llm-anthropic 0.25.1

2026-05-28T23:54:56+00:00

Release: llm-anthropic 0.25.1

New model: Claude Opus 4.8 (claude-opus-4.8).

New -o fast 1 option for fast mode, for organizations with that feature enabled on their account.

Default max_tokens for each model now defaults to that model's maximum output rather than 8,192. #72

See also my notes on Opus 4.8 - I used this new release of llm-anthropic to generate the pelicans.

Tags: llm, anthropic

I think Anthropic and OpenAI have found product-market fit

2026-05-27T16:38:35+00:00

Anthropic are strongly rumored to be about to have their first profitable quarter. Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staff. I think this is because OpenAI and Anthropic have both found product-market fit.

Enterprise customers are now paying API prices

I currently subscribe to the $100/month Max plan from Anthropic and the $100/month Pro plan from OpenAI. If you are a heavy user of coding agents these plans are a fantastic deal. I just ran the ccusage tool on my laptop to get an estimate of how much I would have spent if I were to pay for API tokens in the past 30 days and got:

$1,199.79 for Anthropic Claude Code
$980.37 for OpenAI Codex

That's $2,180.16 worth of tokens for $200 - not bad at all! I'm a moderately heavy user of these tools, but I'm certainly not running agents every hour of the day and night.

I had assumed that companies making extensive use of agents were getting similar discounts. It turns out I could not have been more wrong about that.

I haven't been able to track down the exact date, but at some point in the last six months Anthropic switched their Enterprise plan (originally "Claude seats include enough usage for a typical workday" back in August 2025) to $20/seat/month plus API pricing for usage. This story about the change from The Information is dated Apr 14, 2026, but cites an Anthropic spokesperson claiming that the pricing change occurred in November 2025. Existing customers are finding out about the change as they renew their contracts.

OpenAI made a similar pricing change in April. The Codex rate card (Internet Archive copy) currently says:

Note: On April 2, 2026, we updated Codex pricing to align with API token usage, instead of per-message pricing. This change was applicable to new and existing Plus, Pro, ChatGPT Business and new ChatGPT Enterprise plans.

On April 23, 2026, we made this update for all existing ChatGPT Enterprise plans as well, inclusive of Edu, Health, Gov, and ChatGPT for Teachers.

It's a little harder to decode as they quote prices in "credits", but as far as I can tell those credit costs are an exact match for the API token costs listed for those models.

All of which is to say that as of April 2026 the "Enterprise" cost for both OpenAI Codex and Anthropic Claude Code/Cowork is the same as the listed API price.

GPT-5.5 (released April 23rd) is 2x the API price of GPT-5.4. Opus 4.7 (April 16th) is around 1.4x the price of Opus 4.6 when you take their new tokenizer into account.

So April saw both leading model companies release new frontier models with a higher API price, and both companies now have measures to lock their enterprise customers (who tend to sign year-long deals) at those API prices, not the previous extreme discounts.

I think they've found product-market fit

Why these sudden aggressive moves on pricing? Both Anthropic and OpenAI are planning to IPO, but I suspect there's a more important factor here: I think they've finally found product-market fit, with the coding/general-purpose agent products embodied by Claude Code/Cowork and Codex.

Tools like ChatGPT are wildly popular, but that wild popularity has been difficult to turn into revenue. In February OpenAI boasted more than 900 million weekly active users for ChatGPT, but only 50 million - 5.6% of that - were paying consumer subscribers.

Charging $10-$20/month per user is an OK business, but you'd need 1-2 billion subscribers sticking around for four years to cover $1 trillion in infrastructure.

Companies spending $200+/month/user will get you there a whole lot faster - and as noted above, as a power-user I'm at ~$1,000/month in API costs per vendor already.

Coding agents really did change everything. These are tools which burn vastly more tokens, but are also quickly becoming daily drivers for the work carried out by extremely well-compensated professionals. Right now that's still mostly software engineers, but a coding agent is a tool that can automate anything you can do by typing commands into a computer... so they are clearly applicable to a much wider set of skilled knowledge workers.

As I've discussed on this site at length, the models released in November 2025 elevated agents to being genuinely useful. We've had six months to get used to that idea now - it's no wonder companies are beginning to spend real money on this technology.

You could argue that ChatGPT achieved product-market fit when it became the fastest-growing consumer app in history back in February 2023... but it certainly wasn't making any actual money back then. Coding agents plus enterprise pricing marks the point when these companies start making very real revenue. Maybe even enough to start covering their costs!

And they're ramping up

As further evidence that enterprise agents represent product-market fit for these companies, consider their open job listings.

OpenAI have 703 open jobs right now, of which I'd categorize 229 (32.6%) as relating to enterprise sales and support - account executives, "Go To Market", "Forward Deployed Engineers" and the like.

Anthropic have 390 open jobs, 105 (26.9%) of which look enterprisey to me.

It's pleasingly ironic that these AI labs have picked a business model with such a heavy demand on human labor - enterprise sales contracts don't close themselves without a whole lot of humans in the mix!

(I ran this analysis by scraping their job sites with Claude Code, then having it use Datasette's JSON API to pipe that data into Datasette Cloud where I used Datasette Agent for the analysis, exported here. Dogfood!)

The AI-failure stories around this are pretty thin

I started digging into this in response to a growing volume of stories claiming that large companies were sounding the alarm because their AI usage costs had grown so large.

The most widely cited of these stories appear quite overblown to me.

The most discussed has been Uber, based on this report where CTO Praveen Neppalli Naga indicated that Uber had "maxed out its full year AI budget just a few months into 2026", mostly thanks to Claude Code.

Given that Claude Code only got really good in November it's entirely unsurprising to me that a budget set in 2025 may have failed to predict demand for that tool in 2026!

That Uber story was further fueled by comments made by Uber's COO, Andrew Macdonald, on the Rapid Response podcast. I tracked down the segment and there really isn't much there. Here's what Andrew said:

But then you sometimes go and talk to your senior engineering leaders and you're saying, OK, how many projects that were on the cutting room floor got moved above the line because of the productivity gains because 25% of our code commits were via Claude Code last quarter?

That link is not there yet, right? I think maybe implicitly there's more that is getting shipped. But it's very hard to draw a line between one of those stats and, OK, now we're actually producing like 25% more useful consumer features, right? And that line is hard to draw.

[...] And so if you're not actually able to draw a direct line to how much useful features and functionality you're shipping to your users, that trade becomes harder to justify.

Somehow this fragment turned into headlines like Uber's COO says it's getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remains enormous.

Update 29th May 2026: I edited the above quote to add that last paragraph ending in "becomes harder to justify" on the suggestion of Madison Mills - previously my quoted section stopped at "hard to draw". Here's the full unedited transcript from MacWhisper.

The other popular story around this is Microsoft starts canceling Claude Code licenses, ostensibly to encourage their engineers to dogfood their own Copilot CLI agent instead - but The Verge reporter Tom Warren says "sources tell me the decision is also a financial one", triggered by the June 30th end of Microsoft's financial year.

I think both of these stories support my "product-market fit" hypothesis. The best advice I ever heard on pricing a product was that your customer should suck air through their teeth and then say yes. Uber's budget overrun and Microsoft's seat cancellations look like that effect playing out in practice.

We also know the labs are spending a lot

The big AI labs spend billions of dollars on both training and inference. Credible figures are hard to come by, but we did get one huge hint as to the figures involved from, oddly enough, the recent SpaceX S-1:

[...] in May 2026, we entered into Cloud Services Agreements with Anthropic PBC (“Anthropic”), an AI research and development public benefit corporation, with respect to access to compute capacity across COLOSSUS and COLOSSUS II. Pursuant to these agreements, the customer has agreed to pay us $1.25 billion per month through May 2029 [...]

The Anthropic announcement said that this deal meant they could "increase our usage limits for Claude Code and the Claude API", heavily implying that Colossus is being used for inference, not model training.

Anthropic already have vast amounts of compute from other providers. The fact that they're willing to spend $1.25 billion per month for extra capacity from just one of their vendors hints at how big these inference budgets have become.

API revenue is becoming less important

Over the past two years my impression has been that OpenAI made more of their income from subscription revenue while Anthropic made more from their API.

Anthropic's API revenue was historically quite dependent on a small number of large API customers - this VentureBeat story from August 2025 quotes "sources familiar with the matter" suggesting that just Cursor and GitHub Copilot were responsible for $1.2 billion of the company's then-$4 billion revenue.

Today Anthropic are rumored to hit $10.9 billion in the second quarter, potentially even operating at a profit for the first time.

This pivot-to-Enterprise suggests that the labs have realized that the real money lies in cutting out the middlemen. Anthropic's Claude Code directly competes with Cursor and Copilot. No wonder Cursor are investing in their own models!

April is a new inflection point

I've called November 2025 the November inflection point because that was when GPT-5.1 and Opus 4.5, combined with their respective coding agent harnesses, got good - good enough that we've spent the last six months adapting to agent systems that can reliably get useful work done.

I think April 2026 is a new inflection point where the revenue implications of this have started to land, to the benefit of the frontier AI labs and with material impacts on the budgets of large companies.

We'll know for sure how real this moment is when the S-1 documents for the upcoming Anthropic and OpenAI IPOs give us some real, audited numbers to get our teeth into.

Tags: ai, datasette, openai, generative-ai, llms, anthropic, llm-pricing, coding-agents, claude-code, codex, claude-cowork, november-2025-inflection, datasette-agent, uber

Quoting Corey Quinn

2026-05-26T02:28:54+00:00

I cannot believe I'm saying this, but getting the literal Pope to canonize your product's specific technical limitations as a spiritual treatise is the single greatest act of vendor lobbying I have ever seen.

— Corey Quinn, on Anthropic co-founder Christopher Olah's influence on Magnifica Humanitas

Tags: ai, anthropic, ai-ethics, corey-quinn

Quoting SpaceX S-1

2026-05-20T22:26:36+00:00

We have the ability to use compute resources to support our proprietary AI applications (such as Grok 5, which is currently being trained at COLOSSUS II), while also providing access to select compute capacity to third-party customers. For example, in May 2026, we entered into Cloud Services Agreements with Anthropic PBC (“Anthropic”), an AI research and development public benefit corporation, with respect to access to compute capacity across COLOSSUS and COLOSSUS II. Pursuant to these agreements, the customer has agreed to pay us $1.25 billion per month through May 2029, with capacity ramping in May and June 2026 at a reduced fee. The agreements may be terminated by either party upon 90 days’ notice.

— SpaceX S-1, highlights mine

Tags: ai, generative-ai, llms, anthropic, grok

Behind the Scenes Hardening Firefox with Claude Mythos Preview

2026-05-07T17:56:25+00:00

Behind the Scenes Hardening Firefox with Claude Mythos Preview

Fascinating, in-depth details on how Mozilla used their access to the Claude Mythos preview to locate and then fix hundreds of vulnerabilities in Firefox:

Suddenly, the bugs are very good

Just a few months ago, AI-generated security bug reports to open source projects were mostly known for being unwanted slop. Dealing with reports that look plausibly correct but are wrong imposes an asymmetric cost on project maintainers: it’s cheap and easy to prompt an LLM to find a “problem” in code, but slow and expensive to respond to it.

It is difficult to overstate how much this dynamic changed for us over a few short months. This was due to a combination of two main factors. First, the models got a lot more capable. Second, we dramatically improved our techniques for harnessing these models — steering them, scaling them, and stacking them to generate large amounts of signal and filter out the noise.

They include some detailed bug descriptions too, including a 20-year old XSLT bug and a 15-year-old bug in the <legend> element.

A lot of the attempts made by the harness were blocked by Firefox's existing defense-in-depth measures, which is reassuring.

Mozilla were fixing around 20-30 security bugs in Firefox per month through 2025. That jumped to 423 in April.

Via Lobste.rs

Tags: firefox, mozilla, security, ai, generative-ai, llms, anthropic, claude, ai-security-research, claude-mythos-fable

Notes on the xAI/Anthropic data center deal

2026-05-07T17:09:28+00:00

There weren't a lot of big new announcements from Anthropic at yesterday's Code w/ Claude event, but the biggest by far was the deal they've struck with SpaceX/xAI to use "all of the capacity of their Colossus data center".

As I mentioned in my live blog of the keynote, that's the one with the particularly bad environmental record. The gas turbines installed to power the facility initially ran without Clean Air Act permits or pollution control devices, which they got away with by classifying them as "temporary". Credible reports link it to increases in hospital admissions relating to low air quality.

Andy Masley, one of the most prolific voices pushing back against misleading rhetoric about data centers (see The AI water issue is fake and Data center land issues are fake), had this to say about Colossus:

I would simply not run my computing out of this specific data center

I get that Anthropic are severely compute-constrained, but in a world where the very existence of "AI data centers" is a red-hot political issue (see recent news out of Utah for a fresh example), signing up with this particular data center is a really bad look.

There was a lot of initial chatter about how this meant xAI were clearly giving up on their own Grok models, since all of their capacity would be sold to Anthropic instead. That was a misconception - Anthropic are getting Colossus 1, but xAI are keeping their larger Colossus 2 data center for their own work.

Update 11th June: Anthropic later turned out to have booked capacity in Colossus 2 as well, and Google also bought capacity from xAI.

As an interesting side note, the night before the Anthropic announcement, xAI sent out a deprecation notice for Grok 4.1 Fast and several other models providing just two weeks' notice before shutdown, reported here by @xlr8harder from SpeechMap:

This is terrible @xai. I just spent time and money to migrate to grok 4.1 fast, and you're disabling it with less than two weeks notice, after releasing it in November, with no migration path to a fast/cheap alternative.

I will never depend on one of your products again.

Here's SpeechMap's detailed explanation of how they selected Grok 4.1 Fast for their project in March.

Were xAI serving those models out of Colossus 1?

xAI owner Elon Musk (who previously delighted in calling Anthropic "Misanthropic") tweeted the following:

By way of background for those who care, I spent a lot of time last week with senior members of the Anthropic team to understand what they do to ensure Claude is good for humanity and was impressed. [...]

After that, I was ok leasing Colossus 1 to Anthropic, as SpaceXAI had already moved training to Colossus 2.

And then shortly afterwards:

Just as SpaceX launches hundreds of satellites for competitors with fair terms and pricing, we will provide compute to AI companies that are taking the right steps to ensure it is good for humanity.

We reserve the right to reclaim the compute if their AI engages in actions that harm humanity.

Presumably the criteria for "harm humanity" are decided by Elon himself. Sounds like a new form of supply chain risk for Anthropic to me!

Tags: ai, llms, anthropic, ai-ethics, ai-energy-usage, xai, andy-masley

Live blog: Code w/ Claude 2026

2026-05-06T15:58:27+00:00

I'm at Anthropic's Code w/ Claude event today. Here's my live blog of the morning keynote sessions.

Tags: ai, generative-ai, llms, anthropic, claude, claude-code, live-blog

Quoting Anthropic

2026-05-03T15:13:23+00:00

We used an automatic classifier which judged sycophancy by looking at whether Claude showed a willingness to push back, maintain positions when challenged, give praise proportional to the merit of ideas, and speak frankly regardless of what a person wants to hear. Most of the time in these situations, Claude expressed no sycophancy—only 9% of conversations included sycophantic behavior (Figure 2). But two domains were exceptions: we saw sycophantic behavior in 38% of conversations focused on spirituality, and 25% of conversations on relationships.

— Anthropic, How people ask Claude for personal guidance

Tags: ai, generative-ai, llms, anthropic, claude, ai-ethics, ai-personality, sycophancy

Our evaluation of OpenAI's GPT-5.5 cyber capabilities

2026-04-30T23:03:24+00:00

Our evaluation of OpenAI's GPT-5.5 cyber capabilities

The UK's AI Security Institute previously evaluated Claude Mythos: now they've evaluated GPT-5.5 for finding security vulnerability and found it to be comparable to Mythos, but unlike Mythos it's generally available right now.

Tags: ai, openai, generative-ai, llms, anthropic, claude, ai-security-research, gpt