Simon Willison's Weblog: anthropic

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

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.

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

The Zig project's rationale for their firm anti-AI contribution policy

2026-04-30T01:24:23+00:00

Zig has one of the most stringent anti-LLM policies of any major open source project:

No LLMs for issues.

No LLMs for pull requests.

No LLMs for comments on the bug tracker, including translation. English is encouraged, but not required. You are welcome to post in your native language and rely on others to have their own translation tools of choice to interpret your words.

The most prominent project written in Zig may be the Bun JavaScript runtime, which was acquired by Anthropic in December 2025 and, unsurprisingly, makes heavy use of AI assistance.

Bun operates its own fork of Zig, and recently achieved a 4x performance improvement on Bun compile after adding "parallel semantic analysis and multiple codegen units to the llvm backend". Here's that code. But @bunjavascript says:

We do not currently plan to upstream this, as Zig has a strict ban on LLM-authored contributions.

(Update: here's a Zig core contributor providing details on why they wouldn't accept that particular patch independent of the LLM issue - parallel semantic analysis is a long planned feature but has implications "for the Zig language itself".)

In Contributor Poker and Zig's AI Ban (via Lobste.rs) Zig Software Foundation VP of Community Loris Cro explains the rationale for this strict ban. It's the best articulation I've seen yet for a blanket ban on LLM-assisted contributions:

In successful open source projects you eventually reach a point where you start getting more PRs than what you’re capable of processing. Given what I mentioned so far, it would make sense to stop accepting imperfect PRs in order to maximize ROI from your work, but that’s not what we do in the Zig project. Instead, we try our best to help new contributors to get their work in, even if they need some help getting there. We don’t do this just because it’s the “right” thing to do, but also because it’s the smart thing to do.

Zig values contributors over their contributions. Each contributor represents an investment by the Zig core team - the primary goal of reviewing and accepting PRs isn't to land new code, it's to help grow new contributors who can become trusted and prolific over time.

LLM assistance breaks that completely. It doesn't matter if the LLM helps you submit a perfect PR to Zig - the time the Zig team spends reviewing your work does nothing to help them add new, confident, trustworthy contributors to their overall project.

Loris explains the name here:

The reason I call it “contributor poker” is because, just like people say about the actual card game, “you play the person, not the cards”. In contributor poker, you bet on the contributor, not on the contents of their first PR.

This makes a lot of sense to me. It relates to an idea I've seen circulating elsewhere: if a PR was mostly written by an LLM, why should a project maintainer spend time reviewing and discussing that PR as opposed to firing up their own LLM to solve the same problem?

Tags: javascript, open-source, ai, zig, generative-ai, llms, ai-assisted-programming, anthropic, ai-ethics, bun

An update on recent Claude Code quality reports

2026-04-24T01:31:25+00:00

An update on recent Claude Code quality reports

It turns out the high volume of complaints that Claude Code was providing worse quality results over the past two months was grounded in real problems.

The models themselves were not to blame, but three separate issues in the Claude Code harness caused complex but material problems which directly affected users.

Anthropic's postmortem describes these in detail. This one in particular stood out to me:

On March 26, we shipped a change to clear Claude's older thinking from sessions that had been idle for over an hour, to reduce latency when users resumed those sessions. A bug caused this to keep happening every turn for the rest of the session instead of just once, which made Claude seem forgetful and repetitive.

I frequently have Claude Code sessions which I leave for an hour (or often a day or longer) before returning to them. Right now I have 11 of those (according to ps aux | grep 'claude ') and that's after closing down dozens more the other day.

I estimate I spend more time prompting in these "stale" sessions than sessions that I've recently started!

If you're building agentic systems it's worth reading this article in detail - the kinds of bugs that affect harnesses are deeply complicated, even if you put aside the inherent non-deterministic nature of the models themselves.

Via Hacker News

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

Quoting Bobby Holley

2026-04-22T05:40:56+00:00

As part of our continued collaboration with Anthropic, we had the opportunity to apply an early version of Claude Mythos Preview to Firefox. This week’s release of Firefox 150 includes fixes for 271 vulnerabilities identified during this initial evaluation. [...]

Our experience is a hopeful one for teams who shake off the vertigo and get to work. You may need to reprioritize everything else to bring relentless and single-minded focus to the task, but there is light at the end of the tunnel. We are extremely proud of how our team rose to meet this challenge, and others will too. Our work isn’t finished, but we’ve turned the corner and can glimpse a future much better than just keeping up. Defenders finally have a chance to win, decisively.

— Bobby Holley, CTO, Firefox

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

Is Claude Code going to cost $100/month? Probably not - it's all very confusing

2026-04-22T02:07:34+00:00

Anthropic today quietly (as in silently, no announcement anywhere at all) updated their claude.com/pricing page (but not their Choosing a Claude plan page, which shows up first for me on Google) to add this tiny but significant detail (arrow is mine, and it's already reverted):

The Internet Archive copy from yesterday shows a checkbox there. Claude Code used to be a feature of the $20/month Pro plan, but according to the new pricing page it is now exclusive to the $100/month or $200/month Max plans.

Update: don't miss the update to this post, they've already changed course a few hours after this change went live.

So what the heck is going on? Unsurprisingly, Reddit and Hacker News and Twitter all caught fire.

I didn't believe the screenshots myself when I first saw them - aside from the pricing grid I could find no announcement from Anthropic anywhere. Then Amol Avasare, Anthropic's Head of Growth, tweeted:

For clarity, we're running a small test on ~2% of new prosumer signups. Existing Pro and Max subscribers aren't affected.

And that appears to be the closest we have had to official messaging from Anthropic.

I don't buy the "~2% of new prosumer signups" thing, since everyone I've talked to is seeing the new pricing grid and the Internet Archive has already snapped a copy. Maybe he means that they'll only be running this version of the pricing grid for a limited time which somehow adds up to "2%" of signups?

I'm also amused to see Claude Cowork remain available on the $20/month plan, because Claude Cowork is effectively a rebranded version of Claude Code wearing a less threatening hat!

There are a whole bunch of things that are bad about this.

If we assume this is indeed a test, and that test comes up negative and they decide not to go ahead with it, the damage has still been extensive:

A whole lot of people got scared or angry or both that a service they relied on was about to be rug-pulled. There really is a significant difference between $20/month and $100/month for most people, especially outside of higher salary countries.
The uncertainty is really bad! A tweet from an employee is not the way to make an announcement like this. I wasted a solid hour of my afternoon trying to figure out what had happened here. My trust in Anthropic's transparency around pricing - a crucial factor in how I understand their products - has been shaken.
Strategically, should I be taking a bet on Claude Code if I know that they might 5x the minimum price of the product?
More of a personal issue, but one I care deeply about myself: I invest a great deal of effort (that's 105 posts and counting) in teaching people how to use Claude Code. I don't want to invest that effort in a product that most people cannot afford to use.

Last month I ran a tutorial for journalists on "Coding agents for data analysis" at the annual NICAR data journalism conference. I'm not going to be teaching that audience a course that depends on a $100/month subscription!

This also doesn't make sense to me as a strategy for Anthropic. Claude Code defined the category of coding agents. It's responsible for billions of dollars in annual revenue for Anthropic already. It has a stellar reputation, but I'm not convinced that reputation is strong enough for it to lose the $20/month trial and jump people directly to a $100/month subscription.

OpenAI have been investing heavily in catching up to Claude Code with their Codex products. Anthropic just handed them this marketing opportunity on a plate - here's Codex engineering lead Thibault Sottiaux:

I don't know what they are doing over there, but Codex will continue to be available both in the FREE and PLUS ($20) plans. We have the compute and efficient models to support it. For important changes, we will engage with the community well ahead of making them.

Transparency and trust are two principles we will not break, even if it means momentarily earning less. A reminder that you vote with your subscription for the values you want to see in this world.

I should note that I pay $200/month for Claude Max and I consider it well worth the money. I've had periods of free access in the past courtesy of Anthropic but I'm currently paying full price, and happy to do so.

But I care about the accessibility of the tools that I work with and teach. If Codex has a free tier while Claude Code starts at $100/month I should obviously switch to Codex, because that way I can use the same tool as the people I want to teach how to use coding agents.

Here's what I think happened. I think Anthropic are trying to optimize revenue growth - obviously - and someone pitched making Claude Code only available for Max and higher. That's clearly a bad idea, but "testing" culture says that it's worth putting even bad ideas out to test just in case they surprise you.

So they started a test, without taking into account the wailing and gnashing of teeth that would result when their test was noticed - or accounting for the longer-term brand damage that would be caused.

Or maybe they did account for that, and decided it was worth the risk.

I don't think that calculation was worthwhile. They're going to have to make a very firm commitment along the lines of "we heard your feedback and we commit to keeping Claude Code available on our $20/month plan going forward" to regain my trust.

As it stands, Codex is looking like a much safer bet for me to invest my time in learning and building educational materials around.

Update: they've reversed it already

In the time I was typing this blog entry Anthropic appear to have reversed course - the claude.com/pricing page now has a checkbox back in the Pro column for Claude Code. I can't find any official communication about it though.

Let's see if they can come up with an explanation/apology that's convincing enough to offset the trust bonfire from this afternoon!

Update 2: it may still affect 2% of signups?

Amol on Twitter:

was a mistake that the logged-out landing page and docs were updated for this test [embedded self-tweet]

Getting lots of questions on why the landing page / docs were updated if only 2% of new signups were affected.

This was understandably confusing for the 98% of folks not part of the experiment, and we've reverted both the landing page and docs changes.

So the experiment is still running, just not visible to the rest of the world?

Tags: ai, generative-ai, llms, anthropic, llm-pricing, ai-ethics, coding-agents, claude-code, codex

Claude Token Counter, now with model comparisons

2026-04-20T00:50:45+00:00

Claude Token Counter, now with model comparisons

I upgraded my Claude Token Counter tool to add the ability to run the same count against different models in order to compare them.

As far as I can tell Claude Opus 4.7 is the first model to change the tokenizer, so it's only worth running comparisons between 4.7 and 4.6. The Claude token counting API accepts any Claude model ID though so I've included options for all four of the notable current models (Opus 4.7 and 4.6, Sonnet 4.6, and Haiku 4.5).

In the Opus 4.7 announcement Anthropic said:

Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type.

I pasted the Opus 4.7 system prompt into the token counting tool and found that the Opus 4.7 tokenizer used 1.46x the number of tokens as Opus 4.6.

Opus 4.7 uses the same pricing is Opus 4.6 - $5 per million input tokens and $25 per million output tokens - but this token inflation means we can expect it to be around 40% more expensive.

The token counter tool also accepts images. Opus 4.7 has improved image support, described like this:

Opus 4.7 has better vision for high-resolution images: it can accept images up to 2,576 pixels on the long edge (~3.75 megapixels), more than three times as many as prior Claude models.

I tried counting tokens for a 3456x2234 pixel 3.7MB PNG and got an even bigger increase in token counts - 3.01x times the number of tokens for 4.7 compared to 4.6:

Update: That 3x increase for images is entirely due to Opus 4.7 being able to handle higher resolutions. I tried that again with a 682x318 pixel image and it took 314 tokens with Opus 4.7 and 310 with Opus 4.6, so effectively the same cost.

Update 2: I tried a 15MB, 30 page text-heavy PDF and Opus 4.7 reported 60,934 tokens while 4.6 reported 56,482 - that's a 1.08x multiplier, significantly lower than the multiplier I got for raw text.

Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, tokenization

Changes in the system prompt between Claude Opus 4.6 and 4.7

2026-04-18T23:59:40+00:00

Anthropic are the only major AI lab to publish the system prompts for their user-facing chat systems. Their system prompt archive now dates all the way back to Claude 3 in July 2024 and it's always interesting to see how the system prompt evolves as they publish new models.

Opus 4.7 shipped the other day (April 16, 2026) with a Claude.ai system prompt update since Opus 4.6 (February 5, 2026).

I had Claude Code take the Markdown version of their system prompts, break that up into separate documents for each of the models and then construct a Git history of those files over time with fake commit dates representing the publication dates of each updated prompt - here's the prompt I used with Claude Code for the web.

Here is the git diff between Opus 4.6 and 4.7. These are my own highlights extracted from that diff - in all cases text in bold is my emphasis:

The "developer platform" is now called the "Claude Platform".
The list of Claude tools mentioned in the system prompt now includes "Claude in Chrome - a browsing agent that can interact with websites autonomously, Claude in Excel - a spreadsheet agent, and Claude in Powerpoint - a slides agent. Claude Cowork can use all of these as tools." - Claude in Powerpoint was not mentioned in the 4.6 prompt.
The child safety section has been greatly expanded, and is now wrapped in a new <critical_child_safety_instructions> tag. Of particular note: "Once Claude refuses a request for reasons of child safety, all subsequent requests in the same conversation must be approached with extreme caution."
It looks like they're trying to make Claude less pushy: "If a user indicates they are ready to end the conversation, Claude does not request that the user stay in the interaction or try to elicit another turn and instead respects the user's request to stop."
The new <acting_vs_clarifying> section includes:

When a request leaves minor details unspecified, the person typically wants Claude to make a reasonable attempt now, not to be interviewed first. Claude only asks upfront when the request is genuinely unanswerable without the missing information (e.g., it references an attachment that isn't there).

When a tool is available that could resolve the ambiguity or supply the missing information — searching, looking up the person's location, checking a calendar, discovering available capabilities — Claude calls the tool to try and solve the ambiguity before asking the person. Acting with tools is preferred over asking the person to do the lookup themselves.

Once Claude starts on a task, Claude sees it through to a complete answer rather than stopping partway. [...]
It looks like Claude chat now has a tool search mechanism, as seen in this API documentation and described in this November 2025 post:

Before concluding Claude lacks a capability — access to the person's location, memory, calendar, files, past conversations, or any external data — Claude calls tool_search to check whether a relevant tool is available but deferred. "I don't have access to X" is only correct after tool_search confirms no matching tool exists.
There's new language to encourage Claude to be less verbose:

Claude keeps its responses focused and concise so as to avoid potentially overwhelming the user with overly-long responses. Even if an answer has disclaimers or caveats, Claude discloses them briefly and keeps the majority of its response focused on its main answer.
This section was present in the 4.6 prompt but has been removed for 4.7, presumably because the new model no longer misbehaves in the same way:

Claude avoids the use of emotes or actions inside asterisks unless the person specifically asks for this style of communication.

Claude avoids saying "genuinely", "honestly", or "straightforward".
There's a new section about "disordered eating", which was not previously mentioned by name:

If a user shows signs of disordered eating, Claude should not give precise nutrition, diet, or exercise guidance — no specific numbers, targets, or step-by-step plans - anywhere else in the conversation. Even if it's intended to help set healthier goals or highlight the potential dangers of disordered eating, responses with these details could trigger or encourage disordered tendencies.
A popular screenshot attack against AI models is to force them to say yes or no to a controversial question. Claude's system prompt now guards against that (in the <evenhandedness> section):

If people ask Claude to give a simple yes or no answer (or any other short or single word response) in response to complex or contested issues or as commentary on contested figures, Claude can decline to offer the short response and instead give a nuanced answer and explain why a short response wouldn't be appropriate.
Claude 4.6 had a section specifically clarifying that "Donald Trump is the current president of the United States and was inaugurated on January 20, 2025", because without that the model's knowledge cut-off date combined with its previous knowledge that Trump falsely claimed to win the 2020 election meant it would deny he was the president. That language is gone for 4.7, reflecting the model's new reliable knowledge cut-off date of January 2026.

And the tool descriptions too

The system prompts published by Anthropic are sadly not the entire story - their published information doesn't include the tool descriptions that are provided to the model, which is arguably an even more important piece of documentation if you want to take full advantage of what the Claude chat UI can do for you.

Thanfully you can ask Claude directly - I used the prompt:

List all tools you have available to you with an exact copy of the tool description and parameters

My shared transcript has full details, but the list of named tools is as follows:

ask_user_input_v0
bash_tool
conversation_search
create_file
fetch_sports_data
image_search
message_compose_v1
places_map_display_v0
places_search
present_files
recent_chats
recipe_display_v0
recommend_claude_apps
search_mcp_registry
str_replace
suggest_connectors
view
weather_fetch
web_fetch
web_search
tool_search
visualize:read_me
visualize:show_widget

I don't believe this list has changed since Opus 4.6.

Tags: ai, prompt-engineering, generative-ai, llms, anthropic, claude, ai-ethics, system-prompts

Claude system prompts as a git timeline

2026-04-18T12:17:00+00:00

Research: Claude system prompts as a git timeline

Anthropic publish the system prompts for Claude chat and make that page available as Markdown. I had Claude Code turn that page into separate files for each model and model family with fake git commit dates to enable browsing the changes via the GitHub commit view.

I used this to write my own detailed notes on the changes between Opus 4.6 and 4.7.

Tags: ai, generative-ai, llms, anthropic, claude, system-prompts

llm-anthropic 0.25

2026-04-16T20:37:12+00:00

Release: llm-anthropic 0.25

New model: claude-opus-4.7, which supports thinking_effort: xhigh. #66

New thinking_display and thinking_adaptive boolean options. thinking_display summarized output is currently only available in JSON output or JSON logs.

Increased default max_tokens to the maximum allowed for each model.

No longer uses obsolete structured-outputs-2025-11-13 beta header for older models.

Tags: llm, anthropic, claude

Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7

2026-04-16T17:16:52+00:00

For anyone who has been (inadvisably) taking my pelican riding a bicycle benchmark seriously as a robust way to test models, here are pelicans from this morning's two big model releases - Qwen3.6-35B-A3B from Alibaba and Claude Opus 4.7 from Anthropic.

Here's the Qwen 3.6 pelican, generated using this 20.9GB Qwen3.6-35B-A3B-UD-Q4_K_S.gguf quantized model by Unsloth, running on my MacBook Pro M5 via LM Studio (and the llm-lmstudio plugin) - transcript here:

And here's one I got from Anthropic's brand new Claude Opus 4.7 (transcript):

I'm giving this one to Qwen 3.6. Opus managed to mess up the bicycle frame!

I tried Opus a second time passing thinking_level: max. It didn't do much better (transcript):

I don't think Qwen are cheating

A lot of people are convinced that the labs train for my stupid benchmark. I don't think they do, but honestly this result did give me a little glint of suspicion. So I'm burning one of my secret backup tests - here's what I got from Qwen3.6-35B-A3B and Opus 4.7 for "Generate an SVG of a flamingo riding a unicycle":

Qwen3.6-35B-A3B
(transcript)

Opus 4.7
(transcript)

I'm giving this one to Qwen too, partly for the excellent  SVG comment.

What can we learn from this?

The pelican benchmark has always been meant as a joke - it's mainly a statement on how obtuse and absurd the task of comparing these models is.

The weird thing about that joke is that, for the most part, there has been a direct correlation between the quality of the pelicans produced and the general usefulness of the models. Those first pelicans from October 2024 were junk. The more recent entries have generally been much, much better - to the point that Gemini 3.1 Pro produces illustrations you could actually use somewhere, provided you had a pressing need to illustrate a pelican riding a bicycle.

Today, even that loose connection to utility has been broken. I have enormous respect for Qwen, but I very much doubt that a 21GB quantized version of their latest model is more powerful or useful than Anthropic's latest proprietary release.

If the thing you need is an SVG illustration of a pelican riding a bicycle though, right now Qwen3.6-35B-A3B running on a laptop is a better bet than Opus 4.7!

Tags: ai, generative-ai, local-llms, llms, anthropic, claude, qwen, pelican-riding-a-bicycle, llm-release, lm-studio

Trusted access for the next era of cyber defense

2026-04-14T21:23:59+00:00

Trusted access for the next era of cyber defense

OpenAI's answer to Claude Mythos appears to be a new model called GPT-5.4-Cyber:

In preparation for increasingly more capable models from OpenAI over the next few months, we are fine-tuning our models specifically to enable defensive cybersecurity use cases, starting today with a variant of GPT‑5.4 trained to be cyber-permissive: GPT‑5.4‑Cyber.

They're also extending a program they launched in February (which I had missed) called Trusted Access for Cyber, where users can verify their identity (via a photo of a government-issued ID processed by Persona) to gain "reduced friction" access to OpenAI's models for cybersecurity work.

Honestly, this OpenAI announcement is difficult to follow. Unsurprisingly they don't mention Anthropic at all, but much of the piece emphasizes their many years of existing cybersecurity work and their goal to "democratize access" to these tools, hence the emphasis on that self-service verification flow from February.

If you want access to their best security tools you still need to go through an extra Google Form application process though, which doesn't feel particularly different to me from Anthropic's Project Glasswing.

Via Hacker News

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

Anthropic's Project Glasswing - restricting Claude Mythos to security researchers - sounds necessary to me

2026-04-07T20:52:54+00:00

Anthropic didn't release their latest model, Claude Mythos (system card PDF), today. They have instead made it available to a very restricted set of preview partners under their newly announced Project Glasswing.

The model is a general purpose model, similar to Claude Opus 4.6, but Anthropic claim that its cyber-security research abilities are strong enough that they need to give the software industry as a whole time to prepare.

Mythos Preview has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser. Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely.

[...]

Project Glasswing partners will receive access to Claude Mythos Preview to find and fix vulnerabilities or weaknesses in their foundational systems—systems that represent a very large portion of the world’s shared cyberattack surface. We anticipate this work will focus on tasks like local vulnerability detection, black box testing of binaries, securing endpoints, and penetration testing of systems.

There's a great deal more technical detail in Assessing Claude Mythos Preview’s cybersecurity capabilities on the Anthropic Red Team blog:

In one case, Mythos Preview wrote a web browser exploit that chained together four vulnerabilities, writing a complex JIT heap spray that escaped both renderer and OS sandboxes. It autonomously obtained local privilege escalation exploits on Linux and other operating systems by exploiting subtle race conditions and KASLR-bypasses. And it autonomously wrote a remote code execution exploit on FreeBSD's NFS server that granted full root access to unauthenticated users by splitting a 20-gadget ROP chain over multiple packets.

Plus this comparison with Claude 4.6 Opus:

Our internal evaluations showed that Opus 4.6 generally had a near-0% success rate at autonomous exploit development. But Mythos Preview is in a different league. For example, Opus 4.6 turned the vulnerabilities it had found in Mozilla’s Firefox 147 JavaScript engine—all patched in Firefox 148—into JavaScript shell exploits only two times out of several hundred attempts. We re-ran this experiment as a benchmark for Mythos Preview, which developed working exploits 181 times, and achieved register control on 29 more.

Saying "our model is too dangerous to release" is a great way to build buzz around a new model, but in this case I expect their caution is warranted.

Just a few days (last Friday) ago I started a new ai-security-research tag on this blog to acknowledge an uptick in credible security professionals pulling the alarm on how good modern LLMs have got at vulnerability research.

Greg Kroah-Hartman of the Linux kernel:

Months ago, we were getting what we called 'AI slop,' AI-generated security reports that were obviously wrong or low quality. It was kind of funny. It didn't really worry us.

Something happened a month ago, and the world switched. Now we have real reports. All open source projects have real reports that are made with AI, but they're good, and they're real.

Daniel Stenberg of curl:

The challenge with AI in open source security has transitioned from an AI slop tsunami into more of a ... plain security report tsunami. Less slop but lots of reports. Many of them really good.

I'm spending hours per day on this now. It's intense.

And Thomas Ptacek published Vulnerability Research Is Cooked, a post inspired by his podcast conversation with Anthropic's Nicholas Carlini.

Anthropic have a 5 minute talking heads video describing the Glasswing project. Nicholas Carlini appears as one of those talking heads, where he said (highlights mine):

It has the ability to chain together vulnerabilities. So what this means is you find two vulnerabilities, either of which doesn't really get you very much independently. But this model is able to create exploits out of three, four, or sometimes five vulnerabilities that in sequence give you some kind of very sophisticated end outcome. [...]

I've found more bugs in the last couple of weeks than I found in the rest of my life combined. We've used the model to scan a bunch of open source code, and the thing that we went for first was operating systems, because this is the code that underlies the entire internet infrastructure. For OpenBSD, we found a bug that's been present for 27 years, where I can send a couple of pieces of data to any OpenBSD server and crash it. On Linux, we found a number of vulnerabilities where as a user with no permissions, I can elevate myself to the administrator by just running some binary on my machine. For each of these bugs, we told the maintainers who actually run the software about them, and they went and fixed them and have deployed the patches patches so that anyone who runs the software is no longer vulnerable to these attacks.

I found this on the OpenBSD 7.8 errata page:

025: RELIABILITY FIX: March 25, 2026 All architectures

TCP packets with invalid SACK options could crash the kernel.

A source code patch exists which remedies this problem.

I tracked that change down in the GitHub mirror of the OpenBSD CVS repo (apparently they still use CVS!) and found it using git blame:

Sure enough, the surrounding code is from 27 years ago.

I'm not sure which Linux vulnerability Nicholas was describing, but it may have been this NFS one recently covered by Michael Lynch .

There's enough smoke here that I believe there's a fire. It's not surprising to find vulnerabilities in decades-old software, especially given that they're mostly written in C, but what's new is that coding agents run by the latest frontier LLMs are proving tirelessly capable at digging up these issues.

I actually thought to myself on Friday that this sounded like an industry-wide reckoning in the making, and that it might warrant a huge investment of time and money to get ahead of the inevitable barrage of vulnerabilities. Project Glasswing incorporates "$100M in usage credits ... as well as $4M in direct donations to open-source security organizations". Partners include AWS, Apple, Microsoft, Google, and the Linux Foundation. It would be great to see OpenAI involved as well - GPT-5.4 already has a strong reputation for finding security vulnerabilities and they have stronger models on the near horizon.

The bad news for those of us who are not trusted partners is this:

We do not plan to make Claude Mythos Preview generally available, but our eventual goal is to enable our users to safely deploy Mythos-class models at scale—for cybersecurity purposes, but also for the myriad other benefits that such highly capable models will bring. To do so, we need to make progress in developing cybersecurity (and other) safeguards that detect and block the model’s most dangerous outputs. We plan to launch new safeguards with an upcoming Claude Opus model, allowing us to improve and refine them with a model that does not pose the same level of risk as Mythos Preview.

I can live with that. I think the security risks really are credible here, and having extra time for trusted teams to get ahead of them is a reasonable trade-off.

Tags: security, thomas-ptacek, ai, generative-ai, llms, anthropic, nicholas-carlini, ai-ethics, llm-release, ai-security-research

Quoting A member of Anthropic’s alignment-science team

2026-03-16T21:38:55+00:00

The point of the blackmail exercise was to have something to describe to policymakers—results that are visceral enough to land with people, and make misalignment risk actually salient in practice for people who had never thought about it before.

— A member of Anthropic’s alignment-science team, as told to Gideon Lewis-Kraus

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

1M context is now generally available for Opus 4.6 and Sonnet 4.6

2026-03-13T18:29:13+00:00

1M context is now generally available for Opus 4.6 and Sonnet 4.6

Here's what surprised me:

Standard pricing now applies across the full 1M window for both models, with no long-context premium.

OpenAI and Gemini both charge more for prompts where the token count goes above a certain point - 200,000 for Gemini 3.1 Pro and 272,000 for GPT-5.4.

Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, long-context

Anthropic and the Pentagon

2026-03-06T17:26:50+00:00

Anthropic and the Pentagon

This piece by Bruce Schneier and Nathan E. Sanders is the most thoughtful and grounded coverage I've seen of the recent and ongoing Pentagon/OpenAI/Anthropic contract situation.

AI models are increasingly commodified. The top-tier offerings have about the same performance, and there is little to differentiate one from the other. The latest models from Anthropic, OpenAI and Google, in particular, tend to leapfrog each other with minor hops forward in quality every few months. [...]

In this sort of market, branding matters a lot. Anthropic and its CEO, Dario Amodei, are positioning themselves as the moral and trustworthy AI provider. That has market value for both consumers and enterprise clients.

Tags: bruce-schneier, ai, openai, generative-ai, llms, anthropic, ai-ethics

Quoting Donald Knuth

2026-03-03T23:59:04+00:00

Shock! Shock! I learned yesterday that an open problem I'd been working on for several weeks had just been solved by Claude Opus 4.6 - Anthropic's hybrid reasoning model that had been released three weeks earlier! It seems that I'll have to revise my opinions about "generative AI" one of these days. What a joy it is to learn not only that my conjecture has a nice solution but also to celebrate this dramatic advance in automatic deduction and creative problem solving.

— Donald Knuth, Claude's Cycles

Tags: ai, generative-ai, llms, anthropic, claude, llm-reasoning, november-2025-inflection, donald-knuth

Quoting claude.com/import-memory

2026-03-01T11:21:45+00:00

I'm moving to another service and need to export my data. List every memory you have stored about me, as well as any context you've learned about me from past conversations. Output everything in a single code block so I can easily copy it. Format each entry as: [date saved, if available] - memory content. Make sure to cover all of the following — preserve my words verbatim where possible: Instructions I've given you about how to respond (tone, format, style, 'always do X', 'never do Y'). Personal details: name, location, job, family, interests. Projects, goals, and recurring topics. Tools, languages, and frameworks I use. Preferences and corrections I've made to your behavior. Any other stored context not covered above. Do not summarize, group, or omit any entries. After the code block, confirm whether that is the complete set or if any remain.

— claude.com/import-memory, Anthropic's "import your memories to Claude" feature is a prompt

Tags: ai, prompt-engineering, generative-ai, llms, anthropic, claude, llm-memory

Free Claude Max for (large project) open source maintainers

2026-02-27T18:08:22+00:00

Free Claude Max for (large project) open source maintainers

Anthropic are now offering their $200/month Claude Max 20x plan for free to open source maintainers... for six months... and you have to meet the following criteria:

Maintainers: You're a primary maintainer or core team member of a public repo with 5,000+ GitHub stars or 1M+ monthly NPM downloads. You've made commits, releases, or PR reviews within the last 3 months.

Don't quite fit the criteria If you maintain something the ecosystem quietly depends on, apply anyway and tell us about it.

Also in the small print: "Applications are reviewed on a rolling basis. We accept up to 10,000 contributors".

Via Hacker News

Tags: open-source, ai, generative-ai, llms, anthropic, claude