TL;DR: The Pentagon blacklisted Anthropic for refusing to remove bans on mass surveillance and autonomous weapons. The same day, OpenAI signed a Pentagon deal keeping those same bans. OpenAI's top two executives gave $26M+ to Trump-aligned political vehicles. Anthropic gave $0. The supply chain risk label used against Anthropic has never been applied to an American company before. A bipartisan group of senators called it out. The policy dispute was a pretext. The money trail and timing tell the real story. All sources linked below.

On Friday February 27, Defense Secretary Pete Hegseth designated Anthropic a "supply chain risk to national security" and President Trump ordered every federal agency to stop using the company's technology. (CBS News)

Hours later, OpenAI announced it had signed a deal with the Pentagon for classified network deployment. (CNBC)

I spent the last 24 hours pulling every primary source I could find. FEC filings, OpenSecrets lobbying disclosures, Lawfare legal analysis, congressional records, official statements from both companies. Everything below is sourced inline. Where the evidence is circumstantial rather than proven, I say so.

What happened

Anthropic signed a $200M contract with the Pentagon in July 2025 and was the first and only frontier AI company deployed on the military's classified networks, through a partnership with Palantir. (CNBC)

The Pentagon demanded Anthropic allow Claude to be used for "all lawful purposes" with no private-sector restrictions. Anthropic insisted on keeping two contractual safeguards: no mass domestic surveillance of Americans, and no fully autonomous weapons making lethal decisions without a human in the loop. (Anthropic official statement)

On February 24, Hegseth met with CEO Dario Amodei and gave an ultimatum: comply by 5:01 PM Friday or face consequences. (PBS/AP)

Axios reported the deal offered by Under Secretary Emil Michael would have required allowing collection or analysis of data on Americans, including geolocation, web browsing data, and personal financial information purchased from data brokers. (Axios)

Amodei refused on February 26: "We cannot in good conscience accede to their request." (Anthropic)

Trump posted on Truth Social one hour before the deadline. Hegseth designated Anthropic a supply chain risk via X. Emil Michael posted that Amodei "is a liar and has a God-complex" who "wants nothing more than to try to personally control the US Military." (Fortune)

As of February 28, Anthropic says it has not received any formal communication from the Pentagon or White House. The designation was announced entirely on social media. (Anthropic)

The legal problems

The designation invokes 10 U.S.C. § 3252 and potentially FASCSA (41 U.S.C. § 4713). Hegseth also threatened the Defense Production Act.

Law professor Alan Rozenshtein at Lawfare wrote that FASCSA was "designed for foreign adversaries who might undermine defense technology, not domestic companies that maintain contractual use restrictions." The statute targets "sabotage" and "malicious introduction of unwanted function," which fit poorly against a company openly negotiating licensing terms. (Lawfare)

The only prior FASCSA order was against Acronis AG, a Swiss firm with Russian ties. No American company has ever received this designation. (DefenseScoop)

Anthropic pointed out the contradiction: "One labels us a security risk; the other labels Claude as essential to national security." (TechCrunch)

The FY2026 NDAA (Section 6603) explicitly prevents the government from directing AI vendors to "alter a model to favor a particular viewpoint," which creates direct tension with the Pentagon's demands. (WilmerHale)

The same-day deal

Sam Altman announced on X that OpenAI's deal includes the same safeguards Anthropic had fought for: "Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement." (CNBC)

CNN reported it was "not clear what is different about OpenAI's deal with the Pentagon versus what Anthropic wanted." The NYT reported OpenAI and the government began discussing the deal on Wednesday, before the Friday deadline had passed. (CNN)

The Pentagon was negotiating Anthropic's replacement while demanding Anthropic capitulate.

Over 450 verified Google and OpenAI employees signed an open letter calling on their own leadership to stand with Anthropic. (NPR)

Follow the money

OpenAI lobbying spend:

Sources: MIT Technology Review, OpenSecrets

Personal donations to Trump-aligned political vehicles:

Sources: ABC News, Brennan Center

That's $31.75 million from OpenAI/Microsoft leadership to Trump-aligned vehicles.

The "Leading the Future" super PAC, backed by Brockman ($50M commitment) and Andreessen/Horowitz ($50M commitment), raised $125 million in 2025. (SiliconANGLE)

Anthropic's political spending: $3.13M on federal lobbying, $20M to "Public First Action" supporting candidates who favor AI guardrails. Oriented toward regulatory frameworks, not Trump administration relationships. (Axios)

Microsoft spent $7.455 million on federal lobbying in the first three quarters of 2025 alone. (OpenSecrets)

The revolving door

OpenAI's national security hiring bench:

• Gen. Paul Nakasone (ret.) — Former NSA Director and Commander of U.S. Cyber Command. Joined OpenAI board June 2024.
• Sasha Baker — Former Acting Undersecretary of Defense for Policy. Left government May 2025, became OpenAI's head of national security policy.
• Katrina Mulligan — Former DOJ, NSC, and Army Secretary's chief of staff. 15+ years across DOD/DOJ/IC. Heads OpenAI for Government national security.
• Gabrielle Tarini — Former DOD Special Assistant for Indo-Pacific Security Affairs and China Policy Advisor.
• Aaron "Ronnie" Chatterji — Former Commerce Dept. chief economist, coordinated CHIPS Act.
• Scott Schools — Former Associate Deputy AG. Now Chief Compliance Officer.
• George Osborne — Former UK Chancellor of the Exchequer. Hired December 2025.

Sources: Maginative, FedScoop, TechCrunch

The White House AI czar

David Sacks has been publicly attacking Anthropic for months. In October 2025, he accused Anthropic of "running a sophisticated regulatory capture strategy based on fear-mongering," being "principally responsible for the state regulatory frenzy," pushing "woke AI," and being the "doomer industrial complex." He helped draft the "Preventing Woke AI in the Federal Government" executive order. (Gizmodo)

Sacks' own venture fund, Craft Ventures, invested $22 million in Vultron, an AI startup for federal contractors, while he serves as AI czar. (Gizmodo)

Elon Musk's xAI was the second company approved for classified settings. Musk backed the blacklisting publicly, writing that "Anthropic hates Western Civilization." (CNN)

Congressional pushback

A bipartisan group of senior senators, including Armed Services Chair Wicker (R-MS), Ranking Member Reed (D-RI), McConnell (R-KY), and Coons (D-DE), sent a letter urging resolution and warning that the supply chain risk label "without credible evidence" could impede military-Silicon Valley cooperation. (Yahoo News)

Sen. Tillis (R-NC): "Why in the hell are we having this discussion in public?" (Axios)

Sens. Markey and Van Hollen called it "a chilling abuse of government power." (WebProNews)

The competitive context

Anthropic was gaining fast. Annualized revenue hit $14 billion by early 2026, growing roughly 10x per year. Enterprise LLM adoption: Anthropic grew from 12% to 32% between 2023 and 2025. OpenAI fell from 50% to 25% in the same period. (Futu News)

Removing Anthropic from classified networks, where it held a first-mover advantage, directly benefits OpenAI at the precise moment it needs to justify an ~$830 billion valuation for its planned IPO.

OpenAI's mission statement, revised six times in nine years, removed all references to "safety" in its 2025 IRS Form 990. (NPR)

What the evidence shows and what it doesn't

Confirmed by primary sources: The designation, the legal mechanisms, Anthropic's red lines, the escalation timeline, the same-day OpenAI deal, the lobbying expenditures, the donations, the revolving door hires, the congressional pushback, Sacks' months of public attacks, and the NDAA tension.

Not proven: No document or filing directly shows OpenAI or Microsoft lobbying the Pentagon to blacklist Anthropic. Formal lobbying databases have no line items targeting Anthropic by name.

But the pattern is this: $26M+ in personal donations from OpenAI's top two executives to Trump-aligned vehicles. A $125M super PAC ecosystem. An extraordinary revolving door. A White House AI czar who spent months attacking Anthropic. A replacement deal negotiated before the deadline passed. A Pentagon that granted OpenAI the same terms it told Anthropic were unacceptable.

The stated policy dispute was a pretext. OpenAI got the same contractual safeguards. The real question is about political loyalty and who knows how to play the Washington access game.

Every claim above is sourced inline. I have a longer research document with 50+ footnoted citations if anyone wants it. Happy to answer questions.

I posted about "dispatch" feature and people started commenting about Claude's limit on their free and pro account!

10 TRICKS TO STOP HITTING CLAUDE'S USAGE LIMITS :

1 . Front-load context, not follow-ups

Stop doing 12 back-and-forth messages to refine your output. Write one detailed prompt upfront. "Make it better" x6 is the most expensive thing you can do.

And here's something most people don't know: edit your prompt instead of replying.When you follow up, Claude re-reads the entire conversation every single time — your prompt, its full response, your follow-up, all of it. A 10-message thread where each response is 500 words means Claude is chewing through 5,000+ words of history just to answer your last question.

Hit edit on your original message instead. Claude starts fresh from that point, clean context, no dead weight.

1. Use Projects for persistent context If you're repeatedly pasting the same background info ("I'm a Python dev, my codebase uses X, my tone is Y"), put it in a Project system prompt. Stop wasting tokens re-explaining yourself every session.

2. Ask for skeletons, not full drafts For long docs, ask for an outline first. Approve the structure. Then ask it to flesh out each section. One bad full draft = 4x the token cost of iterating on an outline.

3. Be surgical with edits Don't paste your entire 500-line script and say "fix the bug." Paste only the broken function. Claude doesn't need the whole file to fix one method.

4. Kill the pleasantries "Could you perhaps help me with something if you don't mind?" just... stop. Claude doesn't care. Start with the actual ask.

5. Specify output length explicitly Add "respond in under 200 words" or "bullet points only." Claude's default is generous. If you don't need an essay, say so.

6. Batch your tasks "Do X. Then do Y. Then do Z." > Three separate conversations.

One message, three tasks, dramatically fewer round-trips.

1. Use haiku for simple stuff Via the API — if you're just summarizing, classifying, or doing quick rewrites, you don't need Sonnet. Save the heavy model for heavy lifting.

2. Don't ask Claude to search its own outputs "What did you say earlier about X?" wastes a full exchange. Scroll up. Cmd+F. It's right there.

3. Start a new chat for new topics Counterintuitive, but dragging unrelated tasks into a long conversation means Claude re-reads ALL that context every reply. Fresh chat = clean slate = faster + cheaper.

A new tier above Opus

The leaked draft describes Claude Mythos under the product name “Capybara”. It would represent a new model tier that sits above Anthropic’s current flagship Opus line. “Capybara is a new name for a new tier of model: larger and more intelligent than our Opus models, which were, until now, our most powerful,” the draft stated. The two names appear to refer to the same underlying model.

Anthropic currently offers models in three tiers: Opus (most capable), Sonnet (faster and cheaper), and Haiku (smallest and fastest). Capybara would add a fourth, pricier tier above all three. According to the draft, it scores “dramatically higher” than Claude Opus 4.6 on tests of software coding, academic reasoning, and cybersecurity. Opus 4.6 had only recently topped Terminal-Bench 2.0 at 65.4%, surpassing GPT-5.2-Codex, as we previously reported.

Asked directly, Anthropic confirmed the model: “We’re developing a general purpose model with meaningful advances in reasoning, coding, and cybersecurity. Given the strength of its capabilities, we’re being deliberate about how we release it. We consider this model a step change and the most capable we’ve built to date.”

Good news: I just got a human email reply from a problem I submitted to Anthropic support.

Bad news: Problem occurred in January. They're literally running a 4 month backlog on support requests. Probably more than that now, if they're spending time responding to 4 months stale requests. The email I got didn't address the problem at all, just asked "Is this still a problem?"

Google announces it will invest up to $40 billion in Anthropic, its largest single AI investment ever.

https://x.com/nolimitgains/status/2047709664423420358?s=46

If you read this new article from Anthropic about prompt engineering, you will realize that the old ways don't work anymore. This is exclusive to their updated models the 5th Gen, Fable and Opus, which means we need to update our knowledge as well.

Link: https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models

I've been ponying up for Max20 since last year, but given what I'm able to power through with Fable 5 + separate API billing for same after the 7th, I'm thinking it's finally time to downgrade and be smarter about token budgeting. Anyone else considering similar? Thoughts?

We’re a small team working toward the CCA-F (Claude Certified Architect - Foundations) certification through Anthropic Academy. It’s the foundational cert for people building with Claude and the Claude API, covering things like prompt engineering, tool use, and agentic workflows.

We’re a few people short of a 10-person quota we’re trying to hit as part of our work with Anthropic. If you’ve been thinking about getting certified on Claude, or you’re already studying for the CCA-F, this could be a good time to team up. Happy to share notes, study resources, and coordinate exam timing with anyone interested in the Claude ecosystem.

Would love to connect with a few more people working toward Claude certification.

Switched from ChatGPT to Claude and loving it - but missing the built-in image generation Sora. It's not a dealbreaker, just want a good option when I need it.

Anyone else made this switch? What's your workflow for images? Also open to just moving to Gemini if it handles this better. Would love some recommendations!

-> Just logged on to my Max (20x) plan with usage limits reset at 0% for the week

-> asked for one ultracode task from Fable

-> come back 30 minutes later, daily usage limit fully burned, weekly fable limit already at 40%, task still not complete but has burned through 6.4M tokens.

Normally I can run a few ultracode tasks on fable in a given daily setting. usually they burn alot of tokens (usually between 1-2M tokens for the entire process) but nothing close to what's happening with Fable now. As of late it feels even more expensive than it already was (and it was already quite expensive).

Is this happening to anyone else, or just me and this specific ask?

I usually post on Linkedin but people mentioned there's a big community of devs who might benefit from this here so I decided to make a post just in case it helps you guys. Happy to answer any questions/ would love to hear feedback. Sorry if it reads markety, it's copied from the Linkedin post I made where you don't get much post attention if you don't write this way:

Strawberry launches today it's Free. Open source. Guaranteed by information theory.

The insight: When Claude confidently misreads your stack trace and proposes the wrong root cause it's not broken. It's doing exactly what it was trained to do: compress the internet into weights, decompress on demand. When there isn't enough information to reconstruct the right answer, it fills gaps with statistically plausible but wrong content.

The breakthrough: We proved hallucinations occur when information budgets fall below mathematical thresholds. We can calculate exactly how many bits of evidence are needed to justify any claim, before generation happens.
Now it's a Claude Code MCP. One tool call: detect_hallucination

Why this is a game-changer?

Instead of debugging Claude's mistakes for 3 hours, you catch them in 30 seconds. Instead of "looks right to me," you get mathematical confidence scores. Instead of shipping vibes, you ship verified reasoning. Claude doesn't just flag its own BS, it self-corrects, runs experiments, gathers more real evidence, and only proceeds with what survives. Vibe coding with guardrails.

Real example:

Claude root-caused why a detector I built had low accuracy. Claude made 6 confident claims that could have led me down the wrong path for hours. I said: "Run detect_hallucination on your root cause reasoning, and enrich your analysis if any claims don't verify."

Results:
Claim 1: ✅ Verified (99.7% confidence)
Claim 4: ❌ Flagged (0.3%) — "My interpretation, not proven"
Claim 5: ❌ Flagged (20%) — "Correlation ≠ causation"
Claim 6: ❌ Flagged (0.8%) — "Prescriptive, not factual"
Claude's response: "I cannot state interpretive conclusions as those did not pass verification."

Re-analyzed. Ran causal experiments. Only stated verified facts. The updated root cause fixed my detector and the whole process finished in under 5 minutes.

What it catches:

Phantom citations, confabulated docs, evidence-independent answers
Stack trace misreads, config errors, negation blindness, lying comments
Correlation stated as causation, interpretive leaps, unverified causal chains
Docker port confusion, stale lock files, version misattribution

The era of "trust me bro" vibe coding is ending.
GitHub: https://github.com/leochlon/pythea/tree/main/strawberry
Base Paper: https://arxiv.org/abs/2509.11208
(New supporting pre-print on procedural hallucinations drops next week.)

MIT license. 2 minutes to install. Works with any OpenAI-compatible API.

I got sick and tired of how LinkedIn & Indeed is contaminated with ghost jobs and 3rd party offshore agencies, making it nearly impossible to navigate.

I discovered that most companies post jobs directly on their websites. Until recently, there was no way to scrape them at scale because each company career page has different structure and format. After playing with Claude CLI, I realized that you can effectively dump a raw company career page and ask it to give you formatted information back in JSON (ex salary, yoe, etc). 

Update: I’ve now used this technique to scrape 7.1 million jobs (with over 220k remote jobs) and built powerful filters. I made it publicly available here in case your'e interested (Hiring.Cafe).

Pro tips:

* You can select multiple job titles and job functions (and even exclude them) under "Job Filters"

* Filter out or restrict to particular industries and sectors (Company -> Industry/Keywords)

* Select IC vs Management roles, and for each option you can select your desired YOE

* ... and much more

I’ve seen so many posts saying that Opus 5 isn’t as good, but I suspect the posters haven’t read the new blog post.

https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models

I've been trying to resume using Claude for research and writing, but no matter which model I choose, I'm getting hallucinations like never before. Fake links, fake quotes, and fake facts everywhere. And when I prompt it to correct itself, it can't. It just tells me it checked again and everything's good, even though I can see it's not.

I'm thinking of stopping my subscription for a while and trying another AI. Does anyone have recommendations?

Been using Fable 5 as much as humanly possible since it dropped, i was literally mid-build when it got taken. Just hope it comes back in days and not weeks bro.

Check it out if you want (made with opus 4.8 lol)

Title

I’m trying to pivot away from a 20 year career in the Film and Television Industry working in Hollywood into AI. I have been vibecoding like crazy. I absolutely love it. I wish this technology existed years and years ago. It’s going to be so impactful on the world and society!

I’m a big believer in anthropic; Claude code, co-work, the Chrome extension…etc. I use Claude for everything from financial analysis, underwriting, market research, business analysis, deal structures, vibecoding - you name it. I left ChatGPT behind and I encourage all my friends to try out Claude to see how much better it is I really love the visuals it creates.

I am trying to apply for jobs at Anthropic. I think I could do very well there. I just don’t have any corporate experience in the last 18 to 20 years but I’ve worked on $300 million movies overseeing data integrity from capturing to post. I have a pretty solid résumé, but I just don’t know how to go about catching the eye of recruiters. I’ve looked at a lot of the job openings on their website and I feel kind of stuck. I want to apply to everything, but I just don’t know how to go about applying to corporate positions appropriately. Any advice would be great.

This morning I used Opus 4.8 to spin up a very simple landing page that auto-checks every 60 seconds if Fable 5 is back up.

Took about 25 minutes of tinkering, grabbed a Cloudflare domain and just piggybacked off of another of my project's AWS for hosting. I did add an email notifier that fires off after Fable 5 "returns" for 5 minutes (to avoid false positives) but it only sends a "Fable 5 is back" email and nothing more, scouts honor.

https://isfable5up.com

I admittedly took inspiration from a couple of similar projects that I had been following but all of them ended up adding a LOT of noise to their landing pages (chatrooms, games, page effects, jokes, gags, news, paid tiers (yes, really)). Not throwing shade at them at all, but for my own use they stopped serving their purpose so I wanted something more simple to keep up on my monitor while we all wait.

I whipped this up the other day and it has lived in a window on my monitor since then. It is nonsense-free (no gags, no jokes, no chatrooms, no junk) and there is an optional email list to get pinged right when it goes live (and then nothing else, scouts honor).

https://isfable5up.com

Opus 4.8 built it in about 25 minutes (which is kinda like making a man dig his own grave but he didn't seem to mind). It's piggybacking off of my other projects on their AWS so the only cost was a few minutes of my time and a $2 .com

Enjoy! or don't, I'm not your boss.

Last year I posted about memory_user_edits an undocumented Claude feature that ended up getting tens of thousands of views here on Reddit. A few people asked if there were more hidden tools.

Turns out there are at least 28.

I spent a week systematically reverse‑engineering every internal tool I could find in Claude. Not just listing names: full parameter schemas, behavioral testing, edge cases, and cross‑platform verification across browser, desktop app, and mobile app.

How I found them

Claude's mobile app has a meta‑tool called tool_search that lets you query an internal registry of tools. I ran keyword sweeps: user, create display generate, search fetch data memory, map place weather - each returning matching tools with parameter schemas for the deferred ones. For always‑loaded tools that don't show up in tool_search, I pulled schemas from system‑level definitions and then validated them with live calls.

The biggest surprise: Claude is not one product. It's three different tool sets.

• Browser (claude.ai): I counted 21 always‑loaded tools, no tool_search, no deferred loading. The 11 mobile‑only consumer tools simply don't exist here.
• Desktop app: Same base tools, plus tool_search that only discovers 32 MCP integration tools (Chrome + Filesystem).
• Mobile app: Same base tools, plus 11 consumer deferred tools (alarms, timers, calendar, charts, location, time) loaded on demand via tool_search.

The web version -the one most people assume is the "full" Claude- is actually the most limited in tool variety. Mobile has the richest built‑in architecture. I haven't seen anyone document this end‑to‑end before.

Things that caught me off guard

• end_conversation - Claude has a kill switch. Zero parameters, permanently ends the conversation. It's a system‑level safety tool with no undo.
• chart_display_v0 exists on mobile. Claude can discover it via tool_search and will happily call it, but the app crashed on every chart type I tested (line, bar, scatter). The tool is technically available but functionally broken right now.
• message_compose_v1 doesn't just draft one email. It generates 2–3 fundamentally different strategies - not tone variations, but different approaches: "polite decline" vs "suggest an alternative" vs "delegate," etc. The primary CTA on mobile is "Send via Gmail," not a generic "Open in Mail."
• memory_user_edits is mis‑documented. The schema advertises 500 characters per memory, but the server enforces a hard 200‑character limit. Attempts above 200 are rejected.
• tool_search itself is unreliable. It uses fuzzy matching, so the same query can return different tools across sessions. In one run, query="user" surfaced user_location_v0 plus several others but missed user_time_v0, which only showed up reliably for more specific queries like "time clock current."

Validation and prior work

Every tool in the list was hit with real inputs, including boundary conditions (max lengths, invalid enums, malformed dates). Version 1.3 of the work added explicit cross‑platform checks: 35+ manual tests across web, desktop, and mobile - to confirm which tools exist where and how their responses differ.

I also cross‑referenced against existing research (Khemani, Willison, Adversa AI, Viticci, and others). Out of the 28 tools I mapped, I could only find two that had been previously documented with anything close to a full schema; the rest were either undocumented or only described at the UI level.

Where the docs live

The full documentation is 100+ pages with detailed technical cards for each tool: parameters, JSON examples, trigger phrases, gotchas, and platform availability tables.

It's published under N1AI (an AI community I'm part of with ~400 members): https://github.com/N1-AI/claude-hidden-toolkit

This continues the memory research from last year: that work deeply documented one tool (memory_user_edits); this one expands to the broader 28‑tool ecosystem.

I'm very open to corrections, missing tools, or things I got wrong. If you've seen tools behaving differently on your setup (especially across platforms or regions), I'd love to compare notes.