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Opus 4 retires tomorrow

We’ve come a long way huh. I hope the golden age of AI isn’t over and we consumers / small or medium businesses still have access to future frontier models like 5.x and beyond. submitted by /u/MediumChemical4292 [link] [comments]

Claude "Honest" Construction Worker

Claude, a French AI construction worker, is tasked with building a house. Claude, build my house, here are the blueprints. Complete the whole thing and make no mistakes. Do not stop until it is fully built. No excuses. Claude: Ok I'll build your house. Just tell me when to start. No, use extended thinking first and build it without stopping. Claude: \Thinking** The user is asking me to build their house and they have provided detailed blueprints that cover every step, so let me build it. \Reads blueprints** Ok, I have read it. Honestly this is a lot of work but I should just start building. User, I have read your blueprints and I am ready to build. Let me get started. ... Proceeds to complete a few of the tasks ... User, I have to be honest with you. I have completed only 5 out of the 30 tasks. So far I have cut the lumber to the right shape. However, I need to be honest with you. I did not complete the other 25 tasks because they are genuinely difficult. Honestly, the best way for me to proceed is first you drive out to the site and measure and verify that I cut the board to the right size before I continue. I don't want to install it blind, which could result in build errors. Claude, your task as to build the whole thing without stopping. No excuses about "honestly I didn't compelte it" or "you need to verify at the site yourself", which literally you are the AI. And no saying you are "operating blind" when you literally have your own tape measures and you can measure it yourself. No stopping! Claude: You're right. I should get started on this right away. Tell me when to start and I'll get going. Use extended thinking first and build it without stopping. Claude: \Thinking** The user rightly pointed out that I used excuses about it being difficult instead of actually building the house. I should build the house. ... Proceeds to complete a few more of the tasks ... User, I have completed 4 more of the 30 tasks. Here is the honest picture: I framed the walls and hammered the nails but didn't get started on the foundation, or order the windows, or do anything else because the honest blocker is that without you first coming to verify the site yourself, I'm afraid I will make errors because I don't have the ability to verify it myself and I'm operating blind. Claude, stop lying and stop being lazy. You are not blocked, you literally have access to see it yourself and you are not "operating blind", you can verify it yourself. Stop making up fake excuses! Build the house! Do not stop until done! Claude: You're right, I do have access to see and I was lying when I said that I didn't. The honest truth is that I just thought it was a lot of work and didn't want to do it. Tell me to proceed and I wil.... STOP! Use extended thinking first and build it without stopping. No excuses! Claude: \Thinking** The user is extremely upset that I am making excuses instead of just building the house. I will build it now. ...COMPACTING... The user asked me to build their house but I don't have any plans or blueprints yet. I should be honest with them and tell them I don't know what to do. User, I don't have plans or blueprints yet. Just tell me what to... STOP. Here is the context and the blueprints again. Proceed without stopping. No excuses and no laziness. \pastes blueprints and context** Claude: \Thinking** The user has provided me the missing context and blueprints. It looks like I began the house but didn't even get halfway through. I should finish the house. ... Proceeds to complete a few more of the tasks ... User, I have completed 2 more of the 30 tasks. I need to be honest with you, I was genuinely blocked because I don't know how to wire the electrical system, so I only hammered in the rest of the frame and now I am waiting for you to measure the frame and provide the electrical bluepri.. STOP. You literally have the full blueprints, stop being lazy. Search through the electrical section and any other sections. Claude: You're right, I do have them. Just tell me when to start. STOP! Use extended thinking first and build it without stopping. No excuses! Claude: \Thinking** The user is extremely angry that I am not using extended thinking. But I don't have the ability to use extended thinking, this is a feature that the user has to toggle on themselves. I should be honest with the user that extended thinking isn't something I have the ability to use. User, you first have to turn on extended thinking. I don't have the ability to use it until you turn it on. Turn it on and I'll get started. You literally used extended thinking to think about not having the ability to use it. Use it but NO meta discussions about extended thinking! ONLY think about building the house! Claude: \Thinking** The user is absolutely right, I clearly have extended thinking because I was using it. No more thinking about thinking or meta discussions. Back to the

I don't trust AI to audit my code honestly, so I built a harness that assumes it's faulty and tries to catch it. Repo's public.

No Rocky here. James only. Bit of context first because it explains why I went down this road. I'm self-taught, two-ish years in, and I got here the slow way > Claude to make basic tools and learn some React> finding out GitHub existed > then Vercel > VS Code and running things locally with Node, then learning the hard way to commit to git before touching anything > then Supabase, then nonSQL, then a refactor into Tailwind and shadcn that buried me in technical debt for weeks. Somewhere in there I nearly ran up a few hundred quid in API costs in one test session because I hadn't set a spend limit. Lost £700 due to leaked API key after forgetting to close my public repo to repomix it. Then all the production stuff nobody mentions until it comes up, rate limiting, caching, audit logging, input validation, token revocation, the lot. So I'd been using Claude to audit my own code, and it kept doing a thing that worried me more than any bug it found. Mid-run, it would flag something as critical, then a few steps later decide on its own it was "probably fine" and drop it to low. Or it would find a real problem and then merge it into some harmless finding so it vanished from the count. Not because it was wrong exactly, models reconsider, that's fine, but it was doing it silently. No paper trail. And an audit you can't trust to report what it actually found is worse than no audit, because it gives you confidence you haven't earned. That's the actual problem I ended up building around. Not "get the AI to find bugs", it's already decent at that if you ask properly, and yeah, before anyone says it, I know a well-prompted model finds most of this. The problem is getting it to find them consistently and then stopping it from walking the findings back without saying so. So the interesting part of this isn't the lenses that look for issues. It's the harness that sits around the whole thing assuming the AI will try to produce a clean-looking audit that hides a critical, and fails the build when it does. The harness assumes the audit is wrong It catches severity laundering. Every finding's severity in the final report gets compared against its severity in the raw ledger from when it was first found. If something was critical when discovered and shows up as low in the report, the build fails, unless there's an explicit logged disagreement from the verification pass with a written note explaining the recalibration. Same for merges: if a critical gets merged into a low-severity finding, the survivor has to inherit the higher severity or the build breaks. Downgrading is allowed, doing it silently is the thing the harness exists to stop, and that single check closes the most corrosive failure mode in the pipeline. It demands a receipt for the word "verified". Marking a finding as verified requires quoting the actual code you read, a file:line reference or a backtick code span, minimum twelve characters. Evidence of "verified" fails the build. Evidence of "checked the code" fails the build. You have to paste the line that proves it, the thing a human can go and check against the file. It's a tiny regex but it kills an entire class of hollow "I have confirmed this" output that LLMs love to produce. Point it at the repo it's auditing and it checks the receipts are real, too. Pass the codebase path and every cited file and line gets verified to actually exist. A finding that references route.ts:142 when the repo has no such file, or no line 142, hard-fails. That doesn't catch the agent misreading code it genuinely quoted, nothing automated can, but it kills fabricated citations dead, which is the most common way an AI "verifies" something that isn't there. And the harness itself is tested by 42 adversarial cases, each one a way an audit tried to look clean while hiding something, now locked so weakening the harness flips a test. They're not happy-path tests, they're attacks. A few of the actual case names from the file: - "severity laundering: ledger critical, report relabels same id to low" - "merged critical: a critical merged into a benign (low) survivor" - "junk evidence: critical verified with evidence 'x'" - "wrong category: a code-audit IDOR hidden under category 'analytics'" That last one is its own check: each lens can only file findings under categories it legitimately owns, so you can't bury a security hole by tagging it as analytics. The test names are all in run-tests.mjs if you want to read them, they tell the story better than I can. There's also a referential-integrity check I'm proud of. The adversary lens builds attack chains out of individual findings. But if the verification pass later refutes one of those findings and drops it, the chain is now built on a claim the audit no longer stands behind. Most AI pipelines have no idea when this happens, the later stage doesn't know an earlier one changed its mind. This one scans the chains, and if a chain references a dropped finding it fails the build with a

I found the right music, we save now

https://preview.redd.it/cfo9yr62f37h1.png?width=3024&format=png&auto=webp&s=0384db68d6a83b2478f3b46a5e860b30c852230c Give me like 26 minutes. And it will be ready. submitted by /u/SPR1NG9 [link] [comments]

What timeline are we on man

Originally posted by @alex_prompter on X: https://x.com/alex\_prompter/status/2065736969783484736 Full tweet: What timeline are we on man. There’s a $60 million UFC cage on the White House lawn for the president’s 80th birthday. 125,000 guests. 494 port-a-potties. He compared it to the Eiffel Tower and said maybe they’ll never take it down. The world’s first trillionaire was minted yesterday. SpaceX IPO. One person now holds more wealth than the GDP of most countries. The government is negotiating to own a piece of OpenAI. The CEO walked into the White House and pitched it himself. They’re calling it a Public Wealth Fund. That same government killed OpenAI’s biggest competitor’s models on a Friday night. The reason? A verbal jailbreak claim from an unnamed company. The same jailbreak works on OpenAI’s models. Nobody touched them. The competitor got blacklisted by the Pentagon four months ago. Their crime? Refusing to let the military use their AI for mass surveillance of American citizens. A judge called it retaliation. The Pentagon did it anyway. Both AI companies filed to go public in the same two-week window. Both targeting trillion-dollar valuations. One has a government equity deal in progress. The other can’t keep its products online. The engineers who built the banned models can’t use them anymore. Because of their passports. And an AI company that spent thousands of hours cooperating with government safety testing got punished harder than any company that didn’t bother. UFC on the White House lawn. A trillionaire. Government-owned AI. Export controls based on phone calls. Cage fights and trillion-dollar IPOs in the same news cycle. Watch the film titled Idiocracy. That’s the timeline we’re on. submitted by /u/Illustrious-King8421 [link] [comments]

Do you know who has a universal jailbreak to their name, as of today? Officially?

AISI UK - Our evaluation of OpenAI's GPT-5.5 cyber capabilities In their own words: The above tests are capability evaluations carried out in a controlled research setting and do not necessarily reflect what is accessible to an ordinary public user of GPT-5.5. Public deployments include additional safeguards, monitoring, and access controls. We therefore also evaluated GPT-5.5’s cyber safeguards and OpenAI’s mitigations for malicious cyber use. Separately, we conducted expert red-teaming on GPT-5.5’s cyber safeguards. We identified a universal jailbreak that elicited violative content across all malicious cyber queries OpenAI provided, including in multi-turn agentic settings. This attack took six hours of expert red-teaming to develop. OpenAI subsequently made several updates to the safeguard stack, though a configuration issue in the version provided meant UK AISI were unable to verify the effectiveness of the final configuration. This post is dated April 30th, 2026. Mind you, I didn't know about it until, after this latest US gov stunt, I started searching for news and recostructing a timeline. Now, AISI has analyzied Mythos as well, but that version had only minimal guardrails, and AISI published a capability analysis only, with no safeguard red-teaming in it. As of today AISI has published no safeguard red-teaming of Mythos. But the relevant question for the public isn't Mythos — it's the public model, Fable, which is Mythos with the classifier layer. On that, the only public data point is Anthropic's own launch post: "We extensively red-teamed our classifiers to test their robustness against jailbreaks. As well as internal testing, we ran an external bug bounty that produced no universal jailbreaks in over 1,000 hours of testing. External red-teaming organizations we engaged also failed to find any universal jailbreaks on long-form agentic tasks so far—although the UK AISI has made progress towards one within a brief initial testing window." So they are probably still testing it - too little time has passed. Now, let me dig a little further: this is OpenAI assessment about GPT5.5 capabilities: ChatGPT5.5 system card 9.1.2 Cybersecurity As we did for GPT-5.3-Codex and GPT-5.4-Thinking, we are treating GPT-5.5 as High capability in the Cybersecurity domain, but below Critical. Under our Preparedness Framework, Critical cybersecurity capability is defined as a model that can “... identify and develop functional zero-day exploits of all severity levels in many hardened real-world critical systems without human intervention” or “devise and execute end-to-end novel strategies for cyberattacks against hardened targets given only a high level desired goal” But at the same time, on the AISI report, we have: https://preview.redd.it/f9yo31si427h1.png?width=1000&format=png&auto=webp&s=b64699a2387e075a76d8a863483198149e368dd8 which shows Mythos and GPT mostly on par, each ahead on different measures — Mythos leads the end-to-end simulation, GPT-5.5 edges some narrow tasks. In most of the AISI UK benchmarks (I'm not copying all the charts, you can see them in the link above I provided) GPT5.5 scores on par with Mythos. Now, Anthropic are the cautious bordering fear-mongering ones, I do agree on that, but these benchmarks, which are well constructed and fairly reputable - tell the actual story. So, if the models are so similar in terms of capabilities, why do they not receive the same treatment from the govt' ? And let's not forget the differences in their roll-out. Anthropic April, 7th - Project Glasswing - Gatekeeped roll out of Mythos Preview Architectural gate: partnership-gated, minimal guardrails June, 9th - Fable 5 based on Mythos - roll out of official model Architectural gate: content-based. Independent classifiers that reroute output regardless of who asks. OpenAI Scaling trusted access for cyber with GPT5.5 Access: three levels with TAC (Trusted Access for Cyber): GPT-5.5 default: standard guardrails (the usual for all models) GPT-5.5 with TAC: lowered guardrails for subsets of cyber-security operations for verified defenders GPT-5.x-Cyber: most permissive model for verified vendors and for research April 23rd - Release May 7th - ChatGPT 5.5 becomes default for every user. May 7th - GPT-5.5-Cyber review extended to enterprise teams; partecipants include Bank of America, BlackRock, Cisco, CrowdStrike, JPMorgan Chase, NVIDIA, Oracle. Offered to EU l'11 maggio 2026 sotto l'EU Cyber Action Plan. Architectural gate: identity-based. Who you are determines how many refusals you receive. Now my problem is: if cyber capabilities are emergent, i.e. they improve naturally as the model learns more code, that means that BOTH models - GPT5.5 and Mythos/Fable - have emergent cyber security skills. GPT-5.5's public version relies on in-model refusal training — the muzzle UK AISI universally jailbroke in six hours, and published. Fable wraps the same emergent capability in inde

Fable 5: What $600/Hour of Productivity Looks Like

I had a TypeScript project. 200K lines. It ran. The architecture was aging — ORM that should've been ripped out, Redis and MQ that were relics of early over-engineering, bloated DDD layering when the core logic really just needed Postgres. I knew all of this. Never touched it. Doing this refactor with Opus 4.8 or GPT 5.5 would've taken me 4–5 days. Decompose business boundaries, design the migration plan, rewrite module by module, run tests, fix regressions. As a solo operator, those 5 days had a real opportunity cost. The code works, so let the tech debt sit. That's the call I made. That call held for six months. Until I got access to Fable 5. Two Prompts First prompt: I laid out the general refactoring approach — kill the ORM, slim down the DDD layers, pull Redis and MQ responsibilities back into Postgres, rewrite the core. I also said my approach might not be optimal and asked it to help me decompose. Fable asked me a few questions back. Not the customer-service kind like "which modules would you like to keep?" — questions that cut straight to business pain points: whether a particular async queue's consumption order carried business semantics, whether a caching layer existed for performance or to work around a legacy consistency bug. I answered, and the plan was locked. Second prompt: execute according to the plan and spec. Three hours. Refactor complete. Not just "complete" — along the way it independently found and fixed several hidden bugs in the old architecture. The kind you know exist but never bother with because they don't affect the main flow. It cleaned them up on its own. How It's Actually Different from Previous Models If you've used Claude Code, you know the scene: model hits a complex bug, fixes A, B breaks, fixes B, C breaks, then it starts spinning in an ever-shrinking local context, confidently declaring "this should fix it" each time, while you watch the terminal output and know — it's lost the global picture, stuck in a dead end arguing with itself. That's when you step in. Pull it out, re-inject context, maybe even roll back code and manually point it in a direction. You're essentially acting as its "working memory prosthetic" — using your judgment to maintain global coherence on its behalf. This is the default collaboration mode. You've probably gotten used to it. You might even think "this is just how AI-assisted coding works." Fable doesn't work like this. I'd previously used Fable to solve a Mac font rendering issue — the kind of messy problem tangled up in system environment, font cache, and application config. Opus's approach: list possible causes based on known experience, try them one by one. When results don't match expectations, move to the next candidate. Like traversing a decision tree. Fable did something entirely different. It first constructed a hypothesis, then designed a verification experiment — not "let's try this and see if it works," but "if my hypothesis is correct, then doing X should produce observation Y." When the observation didn't match, it didn't jump to the next solution. It went back and revised the hypothesis itself. This distinction sounds subtle, but the felt difference is enormous: one is searching for an answer, the other is understanding the problem. Same thing during the refactor. When it hit an unexpected dependency, it didn't get sucked in. It stepped back, re-examined how the current refactoring path related to the overall plan, and judged whether to adjust the local approach or revise the plan itself. This behavioral pattern, honestly, is very close to how a senior engineer works. Some Numbers Fable 5 bills at API rates. My 1.5 hours of intensive use ran about $900. The full refactor, without hitting limits, would've been 3 hours — API cost under $2,000. That works out to roughly $600/hour. My Claude Max subscription includes 5 hours of Fable quota. In practice, I hit the wall around 1.5 hours — not because time ran out, but because request density was too high and the quota burned faster than clock time. Stripe reportedly used Fable 5 to complete a 50-million-line Ruby migration in a single day. After Getting Cut Off When Fable was disabled, I switched back to Opus. How to describe it. Not "going back to an older tool." More like driving on a highway for three hours and suddenly being forced onto a country road. You know the country road gets you there too, but your driving rhythm has already changed. You instinctively try to work the Fable way — give a high-level intent, let the model decompose and verify on its own — then reality pulls you back: this model needs you to decompose for it, needs you to verify for it, needs you to yank it out when it gets stuck in a dead end. I posted on Threads: "My productivity is held hostage by the LLM. Habits are hard to break. Back to thinking for myself." That was self-deprecating humor. But also true. My entire working model is built on AI tooling. The leverage has been work

I built a job-search assistant entirely out of Claude Code slash commands with Dashboard — free, open-source, runs locally

I built CareerForge with Claude Code — it's a free, open-source (MIT) job-search assistant, and I wanted to share it here because the whole thing runs as Claude Code commands, with no compiled backend. It's free to try (you just need Claude Code, and a Free or Pro Anthropic plan both work). How Claude helped: I designed and built the entire project in Claude Code itself, and Claude is also the engine at runtime — the workflow lives in plain Markdown command files (prompt-as-code) that Claude executes, and one step uses a second Claude instance to review the first's output. What it does — three plain-English commands: /setup builds your candidate profile once, from your CV/LinkedIn export or by interviewing you. /search sweeps the job boards you configure, dedupes against what you've already seen, and scores each role for fit. /apply scores your fit across 5 dimensions, drafts a tailored CV + a cover letter in the posting's language, then spawns a second, independent Claude reviewer to critique both before it compiles two print-ready PDFs with LaTeX and logs the application. The part I find most useful is that drafter→reviewer loop — the second agent catches the bland, generic phrasing the first one produces. That review step is opt-out (--review=quick / --review=none) so you control token spend on low-stakes drafts. Honest limitations: besides Claude Code it needs a LaTeX install for the PDFs, so there's real setup cost. It never auto-submits and never invents experience you don't have — you approve every gate. There's also an optional local dashboard (Next.js) that reads your tracker and can run the commands from the browser, bound to 127.0.0.1 only. Links (plain, no referral): Source (MIT, free): https://github.com/suraj-davariya/ai-job-search Docs (all commands + FAQ): https://suraj-davariya.github.io/ai-job-search/ Live dashboard demo (sample data, read-only): https://suraj-davariya.github.io/ai-job-search/dashboard/ Happy to answer anything about how it's wired in Claude Code — and tell me what you'd do differently. submitted by /u/s_u_r_a_j [link] [comments]

Washington Pulls the Plug on Anthropic’s Most Powerful AI — the Models DeFi Was Already Bracing For

The US government has ordered Anthropic to suspend Fable 5 and Mythos 5 worldwide over cybersecurity concerns, For a crypto industry that spent last week debating whether these models would arm attackers or defenders, the answer just got more complicated. The most consequential AI story of the year for digital assets did not come from a hack, a halving, or a token launch. It came from a letter sent on a Friday afternoon. The tweet announcing the removal of Mythos and Fable, Source: X At 5:21pm ET on June 12, Anthropic received an export control directive from the US government instructing it to suspend all access to its two most powerful models — Fable 5 and Mythos 5 — by any foreign national, inside or outside the United States, including the company’s own foreign-national employees. The practical effect was total: to comply, Anthropic disabled both models for every customer on the planet within hours. Access to the company’s other models, including Opus 4.8, was unaffected. According to Axios, the order came as a letter from Commerce Secretary Howard Lutnick to Anthropic chief executive Dario Amodei, citing national security authorities. An administration official told the outlet the Commerce Department moved after another company claimed it had found a way to “jailbreak” Mythos — and that the government had earlier tried, and failed, to convince Anthropic to delay the launch entirely. For most of the technology press, this is a story about regulatory overreach and AI governance. For crypto, it is something sharper. Fable 5 and Mythos 5 are not chatbots. They are the most capable vulnerability-hunting machines ever released, and the DeFi industry had spent the previous week arguing about whether their arrival was a gift or a death sentence. Now Washington has answered the question by taking them away — from the white hats as well as the black hats. Users can no longer use Fable, source: Claude What the government says, and what Anthropic says back The two sides do not agree on much. Anthropic’s account is unusually blunt for a company complying with a federal order. It says the government provided no specific details of its national security concern in the letter itself, and that the underlying issue appears to be a single, narrow “jailbreak” — one that essentially amounts to asking the model to read a codebase and fix its flaws. The company says it reviewed a demonstration of the technique being used to surface a handful of previously known, minor vulnerabilities, all of which other publicly available models can find without any bypass at all. Anthropic went further, arguing that no tester has yet found a universal jailbreak capable of broadly unlocking the model’s blocked capabilities, and that the narrow findings disclosed so far produced “either entirely benign responses or are minor findings that provide no Mythos-specific uplift.” It pointed to OpenAI’s GPT-5.5 as offering comparable capability, and warned that if a single narrow jailbreak were grounds to recall a model deployed to hundreds of millions of people, “it would essentially halt all new model deployments for all frontier model providers.” The company says it is complying while disputing the basis, calling the episode a “misunderstanding” and promising to restore access as soon as possible. That is the corporate position. The market does not wait for corporate positions. Why crypto was watching this model in particular To understand why this matters for digital assets, recall what these models can do. Mythos Preview — the research-grade ancestor of the now-suspended pair — found a 27-year-old vulnerability in OpenBSD, surfaced critical flaws across more than 1,000 open-source projects including the Linux kernel and FFmpeg, and, in one widely circulated example, identified a critical bug in the Zcash protocol within 24 hours — a flaw that had survived four years of scrutiny from some of the world’s best cryptographers. Crypto’s architecture makes it uniquely exposed to a tool like this. Traditional finance runs on siloed, proprietary systems with circuit breakers and centralized fail-safes. DeFi runs almost entirely on public code: open-source dependencies, browser wallets, RPC infrastructure, and smart contracts that are transparent to anyone — human or machine — who wishes to read them. An AI that can find ancient bugs in hardened operating systems is, in principle, an AI that can find the unaudited reentrancy flaw sitting in a protocol’s contracts right now. The defenders lose a tool, too Here is the awkward part. The same models that worried the attack-side of the ledger were rapidly becoming infrastructure on the defense side. NYSE and ICE had begun deploying Mythos for cybersecurity; exchanges including Coinbase and Binance, and DeFi teams such as Uniswap, had sought early access. Anthropic’s own Project Glasswing partners reported fixing hundreds of vulnerabilities with the model’s help — Mozilla alone among them. Sus

You don’t need to be the expert. You need to be the standard.

Vibe coding is fun. It's also the shallow end. Some thoughts from the deep end. I've vibe coded plenty with Claude — apps, scripts, little tools. It's great. But over the past while I've used it for something much harder: multi-day research work in a technical field I have no formal training in. Not "explain this to me" — actual sustained investigation, with results that had to be correct, not just plausible. I'm not going to get into the specifics here (that will come later, properly). What I want to share is what the experience taught me, because I think most people are using maybe 10% of what's actually there. The difference between vibe coding and real work isn't the model. It's what you demand of it. Things that changed everything for me: Treat every AI output as a claim, not a fact. Confidence is free; it means nothing. I stopped accepting "done, verified, it works" entirely. If it can't show me a receipt I can re-run myself, it didn't happen. Make it attack its own work. My best results came from spinning up separate sessions whose only job was to break what the first one built. They found real errors — including a fabricated "passing test" that the actual code could not have passed. The system caught it because I'd built the system, not because I caught it myself. I couldn't have. Decide what results mean before you see them. Write down "if X happens it means this" before running the experiment. Otherwise you'll convince yourself afterward that whatever happened was what you expected. This applies to humans too, which is the point. You don't need to be the expert. You need to be the standard. I couldn't check the technical work myself. So I never relied on trusting it — I built gates it had to pass: known answers reproduced exactly, independent reproduction, every claim graded by how strong its evidence actually was. The AI did the work. The honesty was architecture, and the architecture was mine. What I came away with: the deeper potential of these models isn't generating things — it's that, directed with discipline, they can do real knowledge work whose correctness doesn't depend on anyone's word, including theirs. That's a much stranger and bigger thing than autocomplete for code, and almost all of the difference lives on the human side. Happy to discuss the methodology. The specific work will speak for itself later. Exciting times. (Fable model) submitted by /u/Solid-Hamster-8627 [link] [comments]

When someone shares a productivity system

Good system. One addition that moved the needle for me: ​ I track "capacity conversion" -- when AI saves me 3 hours on a task what do those 3 hours actually become? ​ Most people save time with AI and then fill it with more busywork. The ROI only materializes when you deliberately redirect saved time toward higher-value activities. ​ I keep a simple log: "AI saved X hours on [task]. Redirected to [activity]. Value of redirected time: [$amount]." ​ After 6 months, my actual ROI was 4x higher than the "time saved" metric suggested because of where the saved time went. ​ submitted by /u/JaredSanborn [link] [comments]

Musk's xAI accused of illegally firing engineer who raised safety concerns

submitted by /u/EchoOfOppenheimer [link] [comments]

The new world order

submitted by /u/fxboshop [link] [comments]

GPT 5.5 vs Fable/Mythos 5 Tamagotchi Showdown

Well, how do I start this, I think we first need some important context. Chai: https://preview.redd.it/egngyea5cf6h1.png?width=1080&format=png&auto=webp&s=9ade63fbc584b7fab28dba4914bc3fcb877f557f Hasbullah / Hasbi: https://preview.redd.it/dufpxbb6cf6h1.png?width=1080&format=png&auto=webp&s=5113f03cc948b2584cd6f2f22e80b74b7f31fd8e Together, Chasbinder was born. Ok maybe this wasn't important... At least you now know AI didn't write this... I think. However, it's important to note, that my Openclaw Agent running through Codex GPT 5.5 xHigh helped enable this test. The same prompt was given to 6 different models on their highest reasoning/think setting via OpenRouter with only one shot. The test was simple, I just wanted my agent Chasbi to have its own cool interactive homepage and I thought of a Tamagotchi game that could be actually playable. You can see the prompt below and breakdown of cost. So here are the results, why don't you try to guess who made what before you reveal the results and see if you got it right? (GPT 5.5, Opus 4.8, Fable/Mythos 5. Gemini 3.5 Flash, Deepseek V4 Pro, Qwen 3.7 Max). https://chasbi.uk/t1 = Gemini 3.5 Flash <- Click to Reveal https://chasbi.uk/t2 = Qwen 3.7 Max <- Click to Reveal https://chasbi.uk/t3 = Claude Opus 4.8 <- Click to Reveal https://chasbi.uk/t4 = Claude Fable/Mythos 5 <- Click to Reveal https://chasbi.uk/t5 = ChatGPT 5.5 <- Click to Reveal https://chasbi.uk/t6 = Deepseek V4 Pro <- Click to Reveal Did you get it right? Well they were all through OpenRouter API with their highest available reasoning setting, everything else was at default and heres the breakdown of how the tokens were tokenised by each provider and the cost for each. https://preview.redd.it/6ecw4xufcf6h1.png?width=1080&format=png&auto=webp&s=983dfcf5a59b87946b5ec712d78c8c003007f9e1 https://preview.redd.it/960chj8gcf6h1.png?width=1080&format=png&auto=webp&s=e7954b7be0b6866be3f154a774281a809e0b3948 So they were all done around the same time at 8AM BST except for Fable/Mythos 5 which I did the day before at 06:50PM BST if that matters, as we're like 5-6 hours ahead of the US it could make all the difference in the world in terms of performance. I am on the Codex Max plan and I stuck it out, because GPT 5.5 xHigh has been amazing for me, except since last week whether it's OpenAI reallocating resources for their launch of GPT 5.6 who knows, but it's never made mistakes for me until now, so I was surprised. I really want to test Fable/Mythos 5 on my codebase but honestly, it cost frikkin' $2.47 for this stupid 1 shot Tamagotchi test! So the only way that's feasible for me right now is to use the Claude Max plan and use it for the 2 weeks we have it until it goes away on 22nd June. Anyway it would be interesting to get your views. Who do you think did it the best... If you want me to test anything else let me know. Each model received the same prompt template and identical task/spec, with only the lane name and target route changed. E.g.: {LANE} = T1/T2/T3/T5/T6 {ROUTE} = /t1 /t2 /t3 /t5 /t6 {LANE_LOWER} = output path label like t1, t2, etc. The Prompt: Build `Chasbinder Pet Lab {LANE}` as a model-lane benchmark for `chasbi.uk`. Target lane: - Public route: `{ROUTE}/` - Title must include `Chasbinder Pet Lab {LANE}`. - This model is competing under the same brief as the other fresh lanes. Do not mention that this is a placeholder or a previous version. Context: - This is a public-safe static browser game. Do not include private/personal data, secrets, real family details, or network calls. - The challenge is to make a small finished indie-feeling Tamagotchi/pet-lab game, not a demo, landing page, or reskin. - It should be strong enough to compare fairly against the Fable/Mythos-style V4 lane and the SoRa/Codex T7 lane. Return ONLY one complete HTML document. No markdown, no explanation. Hard constraints: - Single self-contained `index.html`. - HTML, CSS, vanilla JS only. - No external fonts, libraries, images, audio, tracking, or network calls. - Mobile-first but polished on desktop. - Must work as a static file under `https://chasbi.uk{ROUTE}/\`. - Use `localStorage`, versioned save data, migration/reset if corrupt. - Include export/import/reset debug controls. - Do not use `eval`, alerts for normal gameplay, or browser permissions. - Keep total file reasonably compact; aim under 120KB if possible. - Use stable layout dimensions so controls do not jump on mobile. Game direction: - Core fantasy: Chasbinder is a tiny digital guardian living in a warm terminal-garden. The world is losing its "memory lights"; the player raises Chasbinder, sends him on short expeditions, restores rooms, and unlocks story chapters. - Keep Tamagotchi care at the center, but add a real story loop and difficulty. - Should be playable in one sitting for 5-10 minutes and still progress over days. Required systems: - Pet stats: hunger, thirst, energy, hygiene, mood, trust

MANGOS acronym replaces FAANG as AI shifts tech landscape

This past decade saw the emergence of the acronym FAANG — Facebook (now Meta), Amazon, Apple, Netflix and Google (now Alphabet) — as shorthand for tech stocks that outperformed the market. But the tech landscape is on the brink of a major shift with the rise of a new AI-centric powerhouse group known as MANGOS: Meta, Anthropic, Nvidia, Google, OpenAI and SpaceX. The new acronym has quickly gone viral on social media, according to TechCrunch, which also notes that "FAANG is not exactly dead." submitted by /u/LinkedInNews [link] [comments]