The Leak That Embarrassed Anthropic

In early April 2026, an unsecured database exposed Anthropic's most ambitious model yet. Claude Mythos — a multimodal AI system described internally as "by far the most powerful AI model we've ever developed" — sat exposed on the internet for a short window before Anthropic's security team caught it and pulled the plug.

The company confirmed it was real. They confirmed it was their most capable system ever. Then they pulled it citing "cybersecurity risks" — a phrase that's now the subject of more than a few Reddit threads and cybersecurity post-mortems.

What Claude Mythos Actually Was

Based on the documents and capabilities that circulated before the shutdown, Claude Mythos was not a simple iteration on Claude 3. It was an architectural step-change:

• Image generation — native image synthesis, not just captioning or understanding. Early testers described output quality competitive with Midjourney V6 at significantly higher resolution ceilings. • Code execution — full sandboxed runtime access, not just code completion. Mythos could execute, debug, and iterate on its own code in real-time with full environment inspection. • Extended reasoning — chain-of-thought depth that reportedly exceeded what had been possible in Claude 3.x by a significant margin. • Multimodal fusion — a unified architecture rather than bolted-together vision + language models.

Why Anthropic Pulled It

When a model this capable is exposed before launch, several things go wrong simultaneously:

• Model weights become reconstructable — any researcher who accessed the database during the exposure window has a copy of capability specifications • Safety eval gaps become public — the specific failure modes, adversarial prompts, and boundary conditions Anthropic found during safety testing are now known • Competitive timeline disrupted — the release schedule, feature set, and positioning against GPT-5 and Gemini Ultra all need to be rebuilt • Regulatory exposure — if the model was accessible externally in any form, it potentially violated compute cluster access agreements

The Community Response

The threads appeared immediately when the leak went public. One post on r/singularity hit 4.5K upvotes and 1K comments within 24 hours. The most discussed question: is this just marketing?

Counterpoint from the technical crowd: the benchmark numbers that leaked alongside the documentation were the actual story. Tests on reasoning tasks, code generation benchmarks, and multimodal evaluations all showed consistent improvements over Claude 3.7 Sonnet by margins that couldn't be faked or cherry-picked.

What This Means for the AI Arms Race

• Multimodal native architectures are the next battlefield • Code execution as a first-class capability changes software development forever • Safety vs. capability tension is accelerating at frontier labs • Leak risk is now a strategic variable every frontier lab must account for

Will Claude Mythos Ever Release?

Anthropic hasn't confirmed a timeline, but the pattern suggests a familiar arc: security audit, architecture review, infrastructure hardening, then a controlled release. The model is real. The capabilities are real. When it comes back, it will be the most watched release in AI history.

Source: OpenClawInstall.AI/blog

I am joining super late to this train…

I have installed OpenClaw on my old laptop that runs as a server with Debian .

I have started with telegram integration and the direct communication works perfectly fine. I wanted to try the discord integration and multi channels for better discoverability - I managed to make it work in DMs but no response in the server itself, even when tagged…

I also tried the multi chats telegram setup but it does t respond there at all…

Am I doing something extremely wrong here?

BTW, I am running on the latest release as for this morning

I know I can use a free api, but hosting it would cost money, or are there free ways to host and use openclaw online?

I’m interested to know your expirience using Open source models with OpenClaw. What are the main problems you encounter if any?

Running OpenClaw v2026.4.10 on a Raspberry Pi with Telegram as my front end. My agent can't execute Python scripts or shell commands live during a conversation — exec policy appears locked to nask=off.

Current workaround is pre-generating context files via cron and having the agent read those instead of executing anything. Fine for static data but useless for anything on-demand.

What I'm trying to figure out:

• Is there a webhook or tool registration approach that lets the agent trigger scripts on user input?
• Anyone gotten dynamic skill execution working from a Telegram session on a recent build?

Already ruled out openclaw cron add (pairing required error) and Telegram getUpdates (OpenClaw drains the queue in real time).

GPT-5.4 Standard dropped March 5 alongside Thinking and Pro variants, but after two months of real use I'm underwhelmed. The price jump is real ($200/mo for Pro) and the reasoning gains over Claude Sonnet 4.6 are marginal for most tasks. Here's where it actually wins and where it doesn't.

Just curious if anyone has actually created an agent like the real JARVIS that connects to PLC’s and other equipment. I’ve created one and put it on git hub. It also can control your pc and apps. I’d like to get some feedback on it. It’s at GitHub.com/thedredgegroup/neximus

It named itself Neximus. I’ve built a local model also using llama 30b. It can also be changed to use any ai api model. I will be adding the ability to use any desktop ai without the need for an api key. If there is any interest. Check it out please and tell me what you think

Anthropic launched Claude Cowork last week as a non-technical alternative to Claude Code. Within days, the open-source community shipped OpenWork — an open-source alternative that does the same thing but without the lock-in.

What Is OpenWork?

OpenWork is a desktop app (macOS, Linux) that wraps OpenCode into a team-friendly experience. Think Claude Cowork but:

• Local-first — Runs on your machine, no cloud dependency
• Bring your own model — Works with Claude, GPT, Gemini, DeepSeek, or local models
• Composable — Desktop app, Slack/Telegram connector, or server mode
• Ejectable — Powered by OpenCode, so everything works without the UI
• MIT licensed — Open source, no commercial restrictions

How It Compares to Claude Cowork

Key Features

• Orchestrator mode — Run OpenCode + OpenWork server without the desktop UI. Install via npm: npm install -g openwork-orchestrator

• Sessions — Create and manage multiple agent sessions with live streaming updates

• Execution plans — OpenCode todos rendered as a timeline, so you can see what the agent is doing and why

• Permissions — Surface permission requests and reply (allow once / always / deny). Critical for team environments.

• Templates — Save and re-run common workflows. Ship repeatable processes across your org.

• Skills manager — Import and manage .opencode/skills folders. Extensible without code changes.

• Debug exports — Copy or export runtime debug reports when something breaks.

Why This Matters

The AI agent tool market is splitting into two camps:

1. Closed ecosystems — Claude Cowork, Codex CLI, Cursor. Great UX, but locked to specific providers.
2. Open alternatives — OpenWork, Open-Codesign, Open Cowork. Less polished, but flexible and provider-agnostic.

For teams that need to:

• Use multiple AI providers (not just Anthropic)
• Keep data on-premise
• Customize agent workflows
• Avoid per-seat pricing

OpenWork is the clear choice.

The Agent Desktop War

Three open-source Claude alternatives launched in the same week:

• OpenWork — Team-focused agent orchestration (this one)
• Open-Codesign — Design-focused alternative to Claude Design
• Open Cowork — Desktop app wrapping Claude Code + others

The open-source community is systematically building alternatives to every Anthropic product. This is good for everyone — it keeps pricing competitive and prevents lock-in.

Availability

• GitHub: different-ai/openwork
• Website: openworklabs.com/download
• npm: npm install -g openwork-orchestrator
• macOS and Linux: Available now
• Windows: Coming (currently paid support plan)

Sources: GitHub different-ai/openwork, openworklabs.com, Anthropic

OpenAI just released GPT-5.5 today and it's a significant step up from 5.4 — especially if you're using Codex for agentic coding. Here's what matters.

The Headline Numbers

GPT-5.5 vs GPT-5.4 vs Claude Opus 4.7:

The Terminal-Bench jump is the one that matters most for Codex users. That benchmark tests complex command-line workflows — planning, iteration, tool coordination — which is exactly what Codex does. Going from 75.1% to 82.7% in one release cycle is a massive leap.

What's Actually New

OpenAI describes GPT-5.5 as "a new class of intelligence for real work and powering agents." The key improvements:

• Agentic coding — GPT-5.5 reaches higher-quality outputs with fewer tokens and fewer retries. On Artificial Analysis's Coding Index, it delivers state-of-the-art intelligence at half the cost of competitive frontier coding models.

• Same latency — Larger models are usually slower. GPT-5.5 matches GPT-5.4 per-token latency in real-world serving while performing at a much higher intelligence level.

• Tool use and planning — Instead of carefully managing every step, you can give GPT-5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going.

• Strongest safety safeguards to date — Evaluated across OpenAI's full safety and preparedness frameworks, with nearly 200 trusted early-access partners providing feedback before release.

Codex CLI v0.124.0 Also Dropped

Alongside GPT-5.5, the Codex CLI got a major update:

• Quick reasoning controls — Alt+, lowers reasoning, Alt+. raises it. Accepted model upgrades now reset reasoning to the new model's default instead of carrying over stale settings.

• Multi-environment sessions — App-server sessions can now manage multiple environments and choose an environment and working directory per turn. Multi-workspace and remote setups are now first-class.

• Amazon Bedrock support — First-class Bedrock integration with AWS SigV4 signing and credential-based auth. If you're running Codex against Bedrock, this is native now.

• Stable hooks — Hooks are now stable, configurable inline in config.toml, and can observe MCP tools as well as apply_patch and long-running Bash sessions.

• Fast service tier default — Eligible ChatGPT plans now default to the Fast service tier unless you explicitly opt out.

What This Means for OpenClaw Users

If you're running OpenClaw with ChatGPT or Codex as your backend:

1. Upgrade your model config — GPT-5.5 is available right now in Codex. Update your model selection to gpt-5.5 and you should see immediate improvements on coding tasks.

2. Cost efficiency — Half the cost of competitive frontier models on the Coding Index means your agent runs cost less per task while producing better output.

3. Fewer retries — The model "checks its work" more effectively, which means fewer loop-and-retry cycles in your agent pipelines.

4. Bedrock users — If you've been routing through Bedrock, the native Codex support means cleaner auth and fewer configuration headaches.

The Bigger Picture

This is the third major model release from OpenAI in a week — ChatGPT Images 2.0 on Tuesday, Privacy Filter yesterday, and now GPT-5.5. They're clearly in a race with Anthropic (Claude Opus 4.7, Mythos Preview) and Google (Gemini 3.1 Pro) to lock down the enterprise and developer market before potential IPOs later this year.

The timing is notable too — the Musk v Altman trial starts Monday in Oakland. OpenAI is clearly trying to establish market momentum before that distraction dominates the news cycle.

Availability

• GPT-5.5: Rolling out today to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex • GPT-5.5 Pro: Rolling out to Pro, Business, and Enterprise users in ChatGPT • API: Coming soon (OpenAI says they're working with partners on safety and security requirements for serving at scale) • Codex CLI: v0.124.0 available now on GitHub

Sources: openai.com, The Verge, 9to5Mac, CNBC, GitHub openai/codex

Hi everyone! 🦞

I’m currently setting up OpenClaw on a VM (Ubuntu) and I’m hitting a bit of a wall with response times and timeouts. I’m hoping to get some recommendations on the best LLM or configuration for my specific hardware.

My Setup:

• Environment: Virtual Machine (VM) accessed via Tailscale.
• CPU: 8 Cores
• RAM: 16GB.
• GPU: None (Pure CPU inference).
• Model Provider: Ollama (local).
• Primary Channel: Telegram.

The Issue: When I run a 7B parameter model (like Qwen 2.5 or Mistral) directly through the Ollama CLI (ollama run), it actually performs quite well—it’s fast enough for my needs. However, as soon as I bridge it through OpenClaw, everything slows down or stops.

I often get stuck in "conjuring" or "moseying" states in the TUI, and the Telegram bot usually times out before receiving the first token. I've tried dropping down to 1.5B models, but I'm still seeing "unknown model" errors or long delays that I don't get in standalone Ollama.

What I'm looking for:

1. Model Recommendations: Which model (3B, 7B, or others) is the "sweet spot" for 8 CPU cores through OpenClaw?
2. Config Tweaks: Are there specific requestTimeout or contextWindow settings you'd recommend for CPU-only setups to prevent OpenClaw from giving up on the model?
3. IronClaw vs. OpenClaw: Given my hardware, should I be looking at the IronClaw version for better performance?

Note: I am strictly looking for a local-only solution. I don’t want to use Gemini, Groq, or other cloud APIs because the rate limits on free tiers are a dealbreaker for me, and I’m not looking to pay for a subscription right now.

Any advice on how to make OpenClaw "patient" enough for CPU inference or which lightweight models handle agents/tools better would be greatly appreciated!

Thanks in advance!

Hoping someone can help. I have OpenClaw fully configured using all free tier tools ie Oracle 24gb server, Openrouter, DuckDuckGo, Discord - I cannot get my agent to do a web search when I ask it to via Discord. Am getting the response: "I cannot perform external web searches or access real-time financial data like currency exchange rates due to security and policy restrictions. My tools are limited to the capabilities explicitly listed, and while web_search exists as a skill, it is not authorized for use in this context." Any ideas? FYI am just setting this up for personal use so want to keep it 100% free.

From my understanding, AIOS is essentially creating your own text-based Jarvis.

Most people say the best code for production based environments is pure Python.

So I wanted to ask how difficult it is to create an AIOS using PURE Python?

No frameworks, like OpenClaw, Nanobot, NanoClaw.

How do I create a safe environment when creating an AIOS?

IDK the difference between using VPS or local or Virtual Machine like Virtual Box (PURE Python).

GPT-5.4 Standard dropped March 5 alongside Thinking and Pro variants, but after two months of real use I'm underwhelmed. The price jump is real ($200/mo for Pro) and the reasoning gains over Claude Sonnet 4.6 are marginal for most tasks. Here's where it actually wins and where it doesn't.

So far, I have successfully connected my orchestrator agent to Telegram. Now, I would like to connect and link several additional sub-agents so that I can have a direct conversation on Telegram (separately from the conversation I have with my orchestrator bot), as well as a new context window for each of them individually.

I haven't been able to figure out exactly how to perform the binding or association process between a Telegram bot and a specific sub-agent.

So now I have a pairing code from the new bot i just created, how do I now associate the TG bot with a specific subagent?

I would appreciate an explanation on exactly how this is done.
10x

I kept hitting the same problem while coding with multiple AI tools.

I’d plan something in Claude, switch to Cursor to implement it, then end up re-explaining the same architecture, rules, and previous decisions all over again.

Same project. Same context. Same wasted tokens.

So I built AgentID for that specific pain.

Now both tools can share:

• project memory
• coding rules
• previous decisions
• active tasks
• handoffs between sessions

Big side effect: much lower token waste because the same context isn’t constantly rebuilt.

Curious if other devs feel this pain too, or if I’m just unusually annoyed by repeated context switching.

PS:

https://preview.redd.it/wzaqta6h41wg1.png?width=1080&format=png&auto=webp&s=562d452bb1ac977a9c55c10413c9276fd4faecf3

Anthropic released Claude Opus 4.7 on April 16 and the official headline sounds great: same price, better model. $5/M input, $25/M output, unchanged from Opus 4.6. But if you're running Opus at scale, there's a catch buried in the release notes that could hit your wallet harder than any price increase.

The Tokenizer Trap

Opus 4.7 ships with a new tokenizer that produces up to 35% more tokens for the same input text. Same paragraph of English. Same Python function. Same JSON payload. Just... more tokens.

Anthropic didn't raise your rate card. They changed how your text gets counted.

Real-world impact: a request that cost $0.10 on Opus 4.6 could cost $0.10 to $0.135 on Opus 4.7, depending on your content mix. Code, structured data, and non-English text get hit hardest. And since output tokens are 5x more expensive than input, the verbosity increase compounds.

Before you migrate production traffic, replay real traffic side by side and measure the actual cost delta. Don't trust the "prices are unchanged" headline.

What Actually Improved

Pricing aside, Opus 4.7 is a meaningful upgrade:

• Agentic coding — The clearest win. Opus 4.7 handles complex, long-running coding tasks with more rigor and consistency. Users report being able to hand off their hardest coding work — the kind that previously needed close supervision — with confidence. It reads codebases, inspects multiple files, forms plans, uses tools, verifies outputs, and revises before finalizing.

• Vision upgrade — First Claude model with high-resolution image support. Ceiling raised from 1568px (1.15MP) to 2576px (3.75MP). Simpler 1:1 coordinate mapping. If you're doing screenshot analysis, diagram understanding, or document processing, this is a significant jump.

• Self-verification — Opus 4.7 checks its own work before reporting back. This is the same pattern we're seeing across the industry (GPT-5.5 does it too). The "plan → execute → verify → report" loop is becoming standard.

• Adaptive thinking — Automatically adjusts how much thinking it uses based on task complexity. Harder problems get more compute. Simpler ones respond fast.

• New xhigh effort level — Sits between high and max. Plus task_budget support for long-running agent loops. More granular control over the quality/speed tradeoff.

• Professional output — Anthropic says Opus 4.7 is "more tasteful and creative" on professional tasks. Higher-quality interfaces, slides, and docs. This matters if you're using Claude for client-facing work.

The Benchmarks

From Anthropic's model card and independent reviews:

• Opus 4.7 lags behind the unreleased Claude Mythos on every axis. Anthropic explicitly states: "Claude Opus 4.7 is less capable than Claude Mythos Preview on every relevant axis we measured." That's an unusual level of honesty from a model launch.

• On the GPT-5.5 comparison table we posted earlier, Opus 4.7 scored 69.4% on Terminal-Bench 2.0 (vs GPT-5.5's 82.7%) and 78.0% on OSWorld-Verified (vs GPT-5.5's 78.7%). Competitive but not leading.

• Where Opus 4.7 still wins: instruction following, long-context retrieval across 1M tokens, and the ability to sustain effort on multi-hour coding tasks without degrading.

Pricing Comparison

Same sticker price. But with the new tokenizer, your effective cost per request goes up 0-35%. Prompt caching (90% discount) and batch processing (50% discount) remain the biggest levers for controlling cost.

Availability

• Claude Pro, Max, Team, and Enterprise users • Claude API (claude-opus-4-7) • Amazon Bedrock • Google Cloud Vertex AI • Microsoft Foundry • GitHub Copilot (rolling out now) • US-only inference available at 1.1x pricing

The Mythos Shadow

The elephant in the room: Anthropic released Opus 4.7 while simultaneously admitting it's not their best model. Claude Mythos — the model that leaked earlier this month — is more powerful on every benchmark. But Anthropic deemed it too dangerous for public release.

Opus 4.7 is what Anthropic considers safe enough to ship at scale. That's either reassuring (they're being responsible) or concerning (the gap between public and private models is growing).

What This Means for OpenClaw Users

If you're running OpenClaw with Claude as your backend:

1. Test before migrating — Replay your actual traffic against Opus 4.7 and measure cost. The tokenizer change is real.

2. Agentic coding is the killer feature — If your workflows involve multi-step coding tasks, Opus 4.7 is a genuine upgrade. The self-verification and sustained effort make it more reliable for autonomous work.

3. Vision workloads — The high-res image support is a significant jump if you're doing screenshot analysis or document processing.

4. Cost control — Prompt caching is your best friend. If you're sending similar system prompts across requests, the 90% discount on cache reads can offset the tokenizer overhead.

Sources: anthropic.com, Mashable, CNBC, Finout, Evolink, GitHub Blog

Hi all,

I’m running a local-first agent setup on a Mac mini M4 with 24GB RAM.

My setup:

• Main orchestrator (cloud): GPT-5.4
• Executor (local): Gemma 4 26B
• Coding agent (local): Qwen3.5:9B
• Also tried Qwen3-Coder:30B, but couldn’t get it to reliably finish tasks

Use cases:

• Sales prospecting based on defined criteria
• Lightweight stock / company research
• Small-to-medium coding tasks
• Productivity workflows (summarising notes, generating reviews)

Issues I’m seeing:

• Long runs timing out
• Context getting messy in multi-step loops
• Outputs look plausible but don’t complete tasks
• Coding agent writes code in chat instead of modifying files
• Runs stall or never finish
• Tool use is much less reliable vs cloud models

Also noticed that larger coding models aren’t consistently better — sometimes less reliable than smaller ones.

Trying to understand if this is:

• Model choice issue
• Config / orchestration issue
• Hardware limitation
• Or just a bad use case for local models right now

Questions:

• Which local models are most reliable for these use cases?
• Any config changes that significantly improve:
  • reliability
  • tool execution
  • long-run stability

Current config (important bits):

Sub-agents:

• runTimeoutSeconds: 1800

Executor (Peter):

• Model: ollama/gemma4:26b
• thinkingDefault: off
• heartbeat: 0m

Coding agent (Jay):

• Model: ollama/qwen3.5:9b
• thinkingDefault: off

Ollama model registry:

Gemma4:26b

• reasoning: false
• contextWindow: 32768
• maxTokens: 16384

Qwen3.5:9b

• reasoning: true
• contextWindow: 65536
• maxTokens: 32768

I’m not expecting cloud-level performance, just trying to get local agents stable enough to be genuinely useful.

Would really appreciate advice from anyone running something similar on Apple Silicon.

Google dropped Gemini 3.1 Pro in April with a reported GPQA Diamond score of 94.3% and 750M users. I tested both on LeetCode hards, React components, SQL queries, and bash scripting. One model clear won 7/10 tasks — and it wasn't the most expensive one.