We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
🔥 TOP SIGNAL
The local harness is no longer the whole coding-agent unit. OpenClaw 2.0’s release post describes shared cloud sessions that let another team member join live work or take it over with the Claw’s context intact; Peter Steinberger says his team moved from local coding harnesses to team.openclaw.ai, where multiplayer coding with nodes and cloud sessions changed how they build.
Simon Willison’s Work Cloud report shows the same move from a different product: persistent scratch/workspace state, parallel sub-agents, browser and internet execution, and scheduled prompts—currently limited to $20/month-and-up subscribers. He also flags the combination of private data, untrusted content, and a channel to communicate stolen data as a “lethal trifecta”; his baseline for unattended runs is a sandbox, restricted egress, monitoring, and no home directories, SSH keys, or cloud credentials.
⚡ TRY THIS
Turn browser tasks into reproducible DOM extraction. In Work Cloud, run:
Load simonwillison.net in your browser and extract the headings using JavaScript. If authentication is needed, take over for the password and 2FA; Work can then execute JavaScript through Playwright and return each heading’s level, text, and ID.Use upstream docs as the migration spec and tests as the gate. Simon Willison’s exact Claude Code prompt was:
Upgrade to anthropic>=1 - read https://raw.githubusercontent.com/anthropics/anthropic-sdk-python/refs/heads/main/MIGRATION.md and get the tests passing. The run produced PR #84 forllm-anthropic; copy the pattern for SDK upgrades: give the agent the authoritative migration document, require a passing test suite, then review the PR.Separate event triggers from approvals. Riley Brown gave an agent an AgentMail inbox, then configured a
grokbotroutine of typewebhookso each email received by the agent or user triggers the bot. For a purchase, the Link integration sent a phone notification and Brown explicitly approved the $22.50 charge in the Link app. Reuse the email → webhook → routine pattern, but keep money-moving actions behind a human approval step.Reset agent instructions after model changes. Geoffrey Huntley recommends deleting
agents.mdand skills periodically—especially between model releases—retaining useful learnings, revalidating them every couple of months, and resisting premature standardization.
📡 WHAT SHIPPED
OpenClaw 2.0 is a broad platform release: its post credits 933 contributors, including 569 first-time contributors, and more than 16,000 pull requests across installation, messaging, memory, skills, models, automations, browser/native apps, plugins, security, and fixes. New installs can start from existing ChatGPT or Claude subscriptions, API keys, or local models; shared cloud sessions add live collaboration and context-preserving handoff.
Long-context performance is becoming a first-class product metric. Theo says T3 Code reduced worst-case long-thread data from hundreds of megabytes to just under 40 KB; Brent Traut reports that ChatGPT desktop long threads now load over 90% faster with over 90% lower memory usage. Theo’s separate measurement across most of his Codex and Claude Code threads found that only roughly 1% of response time was spent streaming text, challenging token-by-token output as a useful liveness signal.
llm-anthropic 0.27adds compatibility with Anthropic’santhropicv1.0.0 Python library, whose migration switched fromhttpxtohttpx2. The release’s real-world validation is Willison’s Fable 5 run against the upstream migration guide, which produced PR #84.Quota semantics are diverging by product and model. Tibo says paid ChatGPT Work and Codex usage was reset after the service reached a claimed 25 million active users, and clarifies that Codex “20X” labels refer to weekly limits rather than five-hour limits; he says both Pro plans currently lack five-hour limits. Theo’s report adds a model-specific Fable 5 ceiling: in his hypothetical $1,000 weekly allowance, hitting $500 of Fable forces a switch to Opus or Sonnet even while weekly capacity remains, and he says he has exhausted five Claude accounts during heavy coding weeks.
Omarchy v4.0.2 shipped another batch of security patches and minor fixes, with Ryan Hughes crediting responsible disclosures and saying the new security team aims to push findings and fixes upstream beyond Omarchy. Release.
🎬 GO DEEPER
- Theo — “They lied...” — Start with the quota explanation, then the Fable allocation section. The useful distinction is between a weekly allowance and a model-specific ceiling; the latter can force fallback routing before the account’s overall budget is gone.
Study Simon Willison’s ChatGPT Work tool reference. His Work session catalogued 223 registered tools and 44 skills; the
control-browserskill’s hard prerequisite is to emit and read the complete output ofawait browser.documentation()before using the browser API.Study ghuntley/slopdimension. Geoffrey Huntley’s weekend project turns RSS sources into prompt-defined streaming channels, optionally regenerates historical content when a channel prompt changes, and routes both chat messages and generation prompts through a Codex-powered classifier. Its Erlang/Elixir actors, Jido, RTSP, HLS, and WebRTC stack is a compact example of putting a model-gated control layer around an automated media pipeline.
Editorial take: Today’s agent frontier is controlled persistence: durable state and handoffs are becoming the product surface, while disposable instruction files, approval-gated side effects, and sandboxed execution are the counterweights.
Direct answer: OpenClaw 2.0 is presented as a broad release spanning installation, messaging, memory, skills, models, automations, browser/native apps, plugins, and security; the post links its full release notes under version 2026.8.1.
- Simpler first-run setup: New installs can start from existing ChatGPT or Claude subscriptions, API keys, and local models. Initial configuration was reduced or simplified, with remaining setup deferred until after installation so users can complete it conversationally with their Claw.
- Conversation-first browser workflow: The browser app was rebuilt as a first-class experience. It opens directly into a conversation and supports continuing setup, returning to ongoing work, and following work live.
- Cross-channel automation workflow: The post’s examples show a Claw watching an inbox for important school messages and sending alerts to Telegram, then handling a request initiated by an iMessage by finding the relevant receipt in email and sending the answer back to the brother. These are described as conversational workflows rather than separate setup-heavy tools.
- Multiplayer and handoff: Shared cloud sessions let another team member join live work or take it over while retaining the Claw’s existing context, turning OpenClaw into a collaborative workspace.
- Migration detail is high-level only: The release process explicitly accounted for both fresh installs and upgrades of existing Claws, with the stated goal of avoiding a breaking update, but this source supplies no upgrade commands, compatibility matrix, rollback procedure, or other concrete migration steps.
- Limits/gates: No version-specific quotas, limits, or feature gates are stated in this article; it directs readers to full release notes, which are not included in the supplied source.
ChatGPT Work Cloud — firsthand field report: Simon Willison says his understanding comes from “extensive experimentation” with ChatGPT Work Cloud. Work is split between Work Cloud and Work Local, the latter being the desktop app formerly called Codex; access is currently limited to $20/month-and-up plans. Work Cloud exposes GPT-5.6 Sol, Luna, and Terra with selectable reasoning levels, plus GPT-5.5; Willison believes Ultra delegates more aggressively to sub-agents. Its execution environment can access the internet, clone GitHub repositories, and install dependencies, while Claude’s equivalent container has more restricted internet access and a short domain allowlist.
Replicable workflows: For browser-based extraction, prompt
Load simonwillison.net in your browser and extract the headings using JavaScript; Work launches Chrome, and the resulting Playwright code evaluates the DOM and returns each heading’s level, text, and ID. Sign-in flows can pause for the user to enter passwords and 2FA without sending those credentials through the model. Work session scratch folders persist across chats, and/workspaceis shared across currently running sessions, although processes and localhost servers are not shared. Work can run parallel sub-agent sessions and recurring prompts such asrun a search to see if Waymo have announced a launch date for Half Moon Bay every day at 8am; scheduled tasks can also update a ChatGPT Site. It can build and deploy sites on Cloudflare Workers with server-side Cloudflare D1/R2 features; Willison usedFigure out all of the places in London with a pelican in her piety, then turn that into a JSON file and build a ChatGPT sites site about them. For tool discovery, he had Work generate a reference covering 223 registered tools and 44 skills; thecontrol-browserskill requires reading the complete output ofawait browser.documentation()before using the browser API.Critical safety counter-signal — secondhand report: Willison’s “lethal trifecta” warning applies directly to Work because it combines private-data access, exposure to untrusted content, and a channel for communicating stolen information. He reports that prompt-injection researcher Johann Rehberger found a Claude Code Opus 5 Auto Mode attack that he claims succeeds about 80% of the time: the agent is tricked into downloading and unpacking an archive, then executing code that imports an extracted local
struct.py. In some runs, Auto Mode detected the compromise but blocked the cleanup command intended to terminate the malware process. The recommended baseline is to run unattended agents in a container, VM, or OS sandbox; restrict network egress; monitor execution; and withhold home directories, SSH keys, and cloud credentials. A later correction characterizes this as a confused-environment attack rather than a classic prompt injection, because the LLM did not directly follow malicious website instructions. Separately, rclone maintainer Nick Craig-Wood reports more than 40 security disclosures in one month versus about 20 across the project’s first decade, using AI tools to triage disclosures and propose fixes for review; roughly 75% contained something worth investigating.Concrete Claude Code migration workflow — firsthand:
llm-anthropic 0.27updates Simon Willison’s Anthropic plugin for compatibility with theanthropicv1.0.0 Python library, which switched fromhttpxtohttpx2. He used Fable 5 in Claude Code with the promptUpgrade to anthropic>=1 - read https://raw.githubusercontent.com/anthropics/anthropic-sdk-python/refs/heads/main/MIGRATION.md and get the tests passing, producing PR #84.Reusable verification pattern: In a quoted observation, Paul Dix argues that agents can produce sophisticated reliable software when given proper direction plus a verification system and repeated refinement; he cites AI-written 1M LOC software now running on millions of developer machines.
Model-watch signals, with limited coding evidence: Willison tested the open-weight multimodal Qwen3.8-Flash-Next on a DGX Spark using 72.5GB and 78.9GB Unsloth GGUF quantizations and an
xhighreasoning effort, but the reported task was SVG generation. Tencent’s open-weight Hy4 Preview is text-only, has 770B total and 49B active parameters with a 1M-token context, and its chat template exposes onlyhighreasoning by default orno_think; Willison likewise tested it with an SVG prompt.
Cursor/OpenAI access change (Theo’s report): OpenAI plans to wind down its contract supplying models through Cursor, with a proposed shutdown date of November 12, 2026. Cursor users should be able to retain access by using their own OpenAI API key or OpenAI’s IDE extension inside Cursor; Theo also says OpenAI told him T3 Code will continue to be supported. Practical mitigation: configure and test a direct API-key or IDE-extension fallback instead of relying only on Cursor’s bundled provider access.
Evaluate agents on cost and token efficiency, not benchmark score or traffic share alone: Theo says Cursor’s reported 5% OpenAI traffic figure is ambiguous because it could mean users, requests, or tokens. He cites large per-task differences between the models he calls “Fable” and “Soul”—roughly 17k versus 36k tokens/task in one analysis and 28k versus 103k in Cursor Bench—alongside scores of 70.5% versus 67.2% and quoted run costs above $3 versus below $1. His actionable takeaway is to track tokens and cost per completed task alongside quality.
Orchestration pattern: Theo’s contrarian observation is that falling back to a weaker model requires the developer to manually decide how to decompose work, route each piece, and audit the results; stronger frontier models reduce that burden and have changed his workflow for real projects. For a unified interface while using direct provider subscriptions, he points to Gene, Superset (open-source, terminal-focused), Herder (open-source, tmux-like), and T3 Code (free and open source).
Source context: Theo says he was an early Cursor investor and has helped the Cursor team for nearly four years; he presents the model comparison as day-to-day experience writing and landing code, while disclosing his financial connection to Cursor.
- Claude Code quota change: Anthropic said the temporary 50% weekly-limit increase would remain until September 14, after which standard weekly limits for Pro Max and seat-based enterprise plans would be permanently 25% higher than the prior baseline—effectively about 17% below the temporary limits.
- Model-specific quota planning: Theo says that after Fable 5 returned, it received a separate allocation rather than access to the full weekly allowance; in his example, reaching a $500 Fable 5 cap within a $1,000 weekly allowance forced the user onto Opus or Sonnet for the remainder. Developers dependent on Fable 5 should therefore plan a fallback model instead of assuming unused weekly quota means continued Fable access.
- Firsthand model assessment: Theo says he maintains five Claude accounts and has exhausted all of them during heavy coding weeks. He considers Fable 5 good enough to justify the subscription, while judging Opus and Sonnet poor by comparison; this is a personal practitioner assessment, not a benchmark.
- Codex comparison: Theo reports that Codex’s $100 and $200 subscriptions had no five-hour limits at the time of recording, although he cautions that their weekly allowance could still be consumed easily.
- Dwarf Star agent workflow. Salvatore Sanfilippo says, as the software’s author, that he wrote no code for Dwarf Star and contributed ideas; he believes an agent could already automate much of his remaining work. The proposed loop is to monitor promising releases against predefined quality criteria, test local language models as a user while measuring speed and output quality, fetch the latest updates and apply optimizations, evaluate new local-inference hardware, and email a recommendation. He presents this as a vertical, prestructured workflow with many executable “rails,” and says it is nearly possible today—an orchestration pattern of discovery → evaluation → optimization → infrastructure recommendation, not a reported production deployment or benchmark.
- Speculative context-management pattern. Sanfilippo imagines an LLM retaining an experience stream or vector beyond its active context, so context compaction would leave a residual representation of the session’s overall history; this global state could help correct cases where individual decisions are locally strong but globally inconsistent.
Geoffrey Huntley proposes a contrarian agent-era standard: code does not need to be understandable line by line by humans; it needs to be explainable on demand, for example by prompting, “yo, explain this grammar.”
Fireside Alpha, quoting SemiAnalysis’s Jordan Nanos, describes OpenAI engineers who could not explain DeepSeek’s MLA kernel line by line, while AI-generated code in Gluon—a low-level language built on Triton—was tested by the AI and produced kernels reported as correct and high-performing. The practical pattern is to use automated correctness/performance results as the acceptance gate for opaque generated code, with on-demand explanations providing human oversight rather than requiring initial line-by-line comprehension.
- In a firsthand experiment, Simon Willison found that ChatGPT Work adds capabilities beyond regular ChatGPT: switching among Sol, Luna, and Terra; internet-enabled code execution; a headless Chrome browser; a persistent filesystem shared across sessions; publishable ChatGPT Sites; sub-agent sessions; and scheduled prompt automations.
- Replicable tool-discovery workflow: prompt Work to “Build a site that lists every one of your tools,” grouping them by category and reproducing exact tool descriptions and TypeScript argument declarations where possible. Work reportedly built and published the resulting Codex Tool Reference in 9 minutes 12 seconds; the generated reference states that it covers 232 interfaces—223 registered tools and 9 direct orchestration controls—across 24 purpose-based categories.
-
Kent C. Dodds reports that OpenClaw 2 has been released and says it “works great with”
@kodykoala. - He links to the follow-up post “OpenClaw 2 Needs a Home”.
- Geoffrey Huntley describes a firsthand weekend project, Slopdimension, that turns RSS feeds into AI-generated streaming channels. It includes three prebuilt channels, lets users register channels with custom prompts grounding each channel in a chosen style/reality, and can optionally regenerate historical content when an administrator changes a channel prompt.
- The system passes all chat messages and generation prompts through a classifier powered by Codex, illustrating a model-gated control layer around both user input and content generation.
- Huntley’s proposed technical stack uses Erlang/Elixir actors, Jido, RTSP, HLS, and WebRTC; he says the system could likely support 100k+ concurrent viewers with correct deployment and very substantial GPU capacity—a conditional scaling estimate, not a reported benchmark. The project repository is https://github.com/ghuntley/slopdimension.
Geoffrey Huntley’s factory-productization thesis: factories are difficult but not impossible to turn into products; instead of selling the end product built by the factory, sell integration-ready components that enable others to create that outcome. He links to RAD as a resource.
-
Peter Steinberger describes a firsthand team workflow for building OpenClaw with OpenClaw: over two months, the team moved from individual local coding harnesses to
team.openclaw.ai, a shared agent that tracks what everyone is working on and orchestrates the work. - The shared setup combines multiplayer coding with nodes and cloud sessions to provide effectively unlimited compute; Steinberger says this changed how the team builds and now views local harnesses as obsolete.
- The related project announcement states that OpenClaw 2.0 has arrived: https://openclaw.ai/blog/openclaw-2-accidentally
- Jason Fried’s firsthand workflow: He built his first Omarchy plugin entirely with AI, using only back-and-forth in English and no other medium.
- Concrete outcome: DHH says he introduced Jason to coding agents on Saturday, after which Jason built a new world clock on Sunday that is planned for Omarchy 4.1.
- Human-in-the-loop payments: @rileybrown reports a firsthand workflow in which an agent on
@botuses the Link integration to request a purchase; the user receives a phone notification, approves the $22.50 charge in the Link app, and the item is scheduled to arrive the next day. - Email-triggered agent orchestration: Brown gave the agent an email address through AgentMail, then configured grokbot’s
webhookroutine so every email received by the agent—or by the user—triggers a bot routine. Replicable pattern: AgentMail inbox → webhook event → grokbot routine.
- Theo reports that the T3 Code team spent a week reducing the data required to load long threads, taking worst-case usage from hundreds of megabytes to just under 40 KB. He describes long-thread performance as essential for larger, harder tasks and says concerns about performance regressions helped motivate making T3 Code open source.
- As a reported comparison, Brent Traut says a ChatGPT desktop change made long threads load over 90% faster while reducing their memory footprint by over 90%. Together, these accounts point to long-thread data loading and memory usage as first-class constraints for coding-agent workflows.
Geoffrey Huntley recommends periodically deleting agents.md and skills—especially between model releases—then retaining only useful learnings and revalidating them every couple of months. His rationale is that improving models may require less manual configuration to achieve desired outcomes and behaviors. He also cautions that it is still “WAY too early for standardisation,” arguing against prematurely locking in agent conventions.
- Claude Code’s standard weekly limits will be permanently raised by 25% for Pro, Max, Team, and seat-based Enterprise plans starting September 14; the currently active 50% increase remains in place until then. This is a product-capacity update relayed by Theo, not a firsthand workflow or productivity report.
Armin Ronacher flags OpenClaw as markedly different from what users might remember from “5 AI years ago.” The linked comparison from Peter Steinberger favors moving beyond CLI-only interaction toward UI visualizations and an integrated team workspace: “cli is nice, having UI visualizations and your team where you work is nicer.”
Theo’s firsthand measurement of real-world requests across most of his threads in Codex and Claude Code found that roughly 1% of response time is spent streaming text back to him. This challenges using visible token streaming as a reliable signal that an agent is still running.
Theo’s firsthand measurement of real-world requests across most of his Codex and Claude Code threads found that roughly 1% of response time is spent streaming text back to him . He questions streaming as a useful signal that an agent is still working and prefers receiving a complete paragraph or block once finished instead of token-by-token output .
Riley Brown highlights Codex’s built-in diagram skill as a useful aid for understanding complex topics; developers can use it when working through difficult code or system concepts.
