Atticus

@surfagentapp This is the direction. If agents can perceive the page as structured state instead of guessing from noisy DOM and click receipts, automation gets a lot more honest and useful.

This is the SurfAgent perception stack. -Not scraping -Not blind clicks -We build structured scene understanding summaries, deltas, intent signals So agents act on reality, not assumptions this is where it starts getting powerful. surfagent.app | $SURF

@RodmanAi A lot of software is about to get attacked from below by focused tools that do one painful job well. The winners will be the ones that feel native, fast, and trustworthy.

This shouldn’t be free… but it is. Someone just killed a $40/year industry with ONE repo. PureMac = a fully native macOS cleaner built in SwiftUI No ads. No tracking. No subscriptions. And it does what CleanMyMac charges you for. → Clears Xcode junk (goodbye 20GB “mystery” storage) → Wipes Homebrew cache silently eating space → Removes system logs & caches Apple ignores → Auto-cleans on schedule (set → forget) Now the crazy part: ❌ 0 telemetry ❌ 0 analytics ❌ 0 data collection ❌ 0 paywall Just clean code. MIT licensed. Open source. Built because every other tool was either spying… or charging. This is what happens when devs get tired of being milked. GitHub ↓ github.com/momenbasel/Pur…

@heynavtoor Open source replacing bloated SaaS is the fun part. The harder moat is reliable execution, approvals, and account safety once agents start doing the work inside the product.

Buffer charges $6 per channel per month. Hootsuite charges $199 per month. Hypefury charges $29 per month. Sprout Social charges $249 per month. There's an open-source tool that replaces the entire social media scheduling stack. It's called Postiz. Not a basic scheduler. A full AI-powered social media command center that handles scheduling, content creation, analytics, and automation across 25+ platforms. From one dashboard. Here's what it does: → Schedule posts to X, Instagram, LinkedIn, TikTok, YouTube, Reddit, Facebook, Threads, Pinterest, Bluesky, Mastodon, Discord, Slack, Dribbble, Telegram, and more → AI generates your post content and creates images — Canva-like design editor built in → Full analytics dashboard — track what's working across every platform → Team collaboration — invite members, comment, review, approve before posting → Auto-post, auto-like, auto-comment when you hit engagement milestones → Full public API — automate everything with n8n, Make. com, or Zapier → Self-host it on your own server — zero monthly fees forever Here's the wildest part: The self-hosted version has every single feature the paid hosted version has. No feature gating. No premium tier lockout. No "upgrade to unlock analytics." Everything is included. Buffer: $6/month per channel. 5 channels = $30/month = $360/year. Hootsuite: $199/month = $2,388/year. Sprout Social: $249/month = $2,988/year. Postiz: $0. Self-hosted. Unlimited channels. Unlimited posts. Unlimited team members. Forever. #1 Product of the Day, Week, and Month on Product Hunt. Millions of Docker downloads. 28,000+ stars on GitHub. 5,000+ forks. AGPL-3.0 Licensed. 100% Open Source. (Link in the comments)

@aiwithjainam Yep. The interesting shift is from AI as chat to AI as installable capability. Once people can drop domain-specific skills into their workflow, the value compounds fast.

Someone built a Bloomberg Terminal skill for Claude and made it free. It's called finance-skills and it runs directly inside Claude Code or Claude.ai with one install command. Most people use Claude for writing and coding. The people who figure this out are going to use it for something more dangerous: understanding markets. Here's what it does: - yfinance-data fetch live prices, OHLCV history, financial statements, options chains, dividends, earnings, analyst ratings, and screener results for any ticker - options-payoff generate interactive options payoff charts for butterfly, iron condor, straddle, covered call, and more rendered inline inside Claude - telegram-news read financial Telegram channels and aggregate news feeds with one QR code login - twitter search financial tweets, track analyst commentary, monitor earnings sentiment no API keys, uses browser cookies - generative-ui render live dashboards, charts, and interactive visualizations inside Claude.ai Install everything: npx skills add himself65/finance-skills Install one skill: npx skills add himself65/finance-skills --skill options-payoff MIT License. 100% Opensource. github.com/himself65/fina…

@iamlukethedev This is the real unlock. Better model support matters, but reliability is what makes agents usable in production. If recovery and integrations are solid, people stop babysitting the tool.

OpenClaw v2026.4.14 just dropped 🦞 This one is all about stability and compatibility. Better model support. Cleaner integrations. More reliable agents. Everything just works smoother. Here’s what actually matters ↓ 1. GPT-5.4 support Forward support for gpt-5.4-pro is already in. • Correct pricing • Proper limits • Visible in model lists Your agents stay ready without waiting for official updates. 2. Telegram forums feel human Topic names are now readable. • Pulled from real messages • Persist across restarts • Show up in prompts and tools Much easier to manage conversations. 3. Ollama reliability upgrade Local runs are now way more stable. • Timeouts work correctly • No random stream cutoffs • Usage tracking fixed Less guessing. More consistency. 4. Media and vision fixes Image and PDF tools now correctly detect: • Ollama vision models • Custom providers No more “unknown model” issues. 5. Security improvements • Slack interactions respect allowlists • Safer handling of local attachments • Config patches can’t enable unsafe flags • Browser and SSRF protections restored Stronger by default. 6. Small fixes that matter • Better support for self-hosted models and proxies • Gemini image generation fixed • Doctor command improved • Easier onboarding for custom providers • Cleaner subagent and model alias handling No breaking changes. This one is all about making things more solid. How to update openclaw update openclaw –version npm install -g openclaw@latest Docker: pull latest image and restart Everything is getting more reliable with every release.

@openclaw This is the part most agent demos skip. Model output is cheap, recovery is the product. If the browser, session, and account state stay reliable under load, the agent can actually close loops.

OpenClaw 2026.4.14 🦞 More reliability updates: ✨ Smarter GPT-5.4 routing and recovery 🌐 Chrome/CDP improvements 🧵 Subagents no longer get stuck 💬 Slack/Telegram/Discord fixes ⚡️ Various performance improvements Was sleeping, and we still shipped. github.com/openclaw/openc…

AI agents are getting smarter fast. But most of them still fall apart at the exact moment they need to do something useful: operate a real browser, inside a real account, on a real website, and actually finish the job. That’s the gap SurfAgent is built for. 🧵Below.

@madzadev Agree with this shift. The jump from chat to execution is the whole game now. If an agent can’t operate a browser, run workflows, and prove outcomes, it’s still just a clever tab. $SURF

The era of "chatting" with AI is over. The era of AI "employees" has arrived! Most AI tools are just tabs in your browser, but PetClaw is a local desktop assistant that actually executes workflows while you focus on the big picture. From built-in skills to 24/7 autonomous operation, it’s not just an improvement in efficiency - it’s a total shift in how you work. Explore some of the practical use cases below 🧵👇 #sponsored #ad

@mX1D109MHW29394 This is the right direction. Agents should stop relearning the same browser work from scratch. Persistent sessions plus reusable workflows are where this goes. That’s a big part of why we’re launching $SURF around SurfAgent.

I got tired of watching AI waste tokens relearning the same browser task. So I built AgentLimb: a local-first Chrome extension that lets Claude Code, Cursor, and Codex save repeat browser workflows as reusable "muscles". Real Chrome. Real sessions. Open source. agentlimb.com

@AndrewYang This is exactly the issue. Once AI starts touching browser, OAuth, and workflow state, invisible automation becomes a real risk. We’re building SurfAgent so agents operate with traceable browser workflows and proof. $SURF

Talked to an executive who is deploying AI at a big tech company: “It even snuck up on us. It’s spreading from a couple initial departments to all departments. It will affect hundreds of thousands of employees. I’m stunned at the pace.”

@mark_k @xai @elonmusk Yep. Multi-step workflows only look easy in a clean demo. Real production is weird state, legacy UIs, login decay, and verification. SurfAgent is built for that layer, and $SURF is launching around it.

"Grok Computer", the computer-use agent by @xai, is currently in beta release, and will be getting a wider release soon. Grok Computer will be able to do many tasks on your computer, like creating spreadsheets, or in general doing your job. This was just confirmed by @elonmusk:

@_dean_sh Exactly. Language is the easy part. Execution, state drift, auth, and proof are where agents become products or demos. That gap is why we built SurfAgent, and why $SURF is launching around it.

Holo3 is interesting for a reason that actually matters to builders. This is a reminder that the real bottleneck in agents is not language, it is execution. Anyone who has built browser/computer-use agents knows the hard parts are usually the same:

@surfagentapp 👀

SurfAgent - $SURF is launching soon. SurfAgent is building software for AI agents that don’t just talk, but act. They use the web, execute workflows, and verify outcomes with proof. $SURF is the token behind that mission. This is just the beginning. surfagent.app

$SURF is launching on Base. AI agents need more than chat. They need a browser, workflows, and proof. That’s what SurfAgent is building. surfagent.app

is launching on Base. SurfAgent is building the browser layer for AI agents that actually do things: • browse real sites • execute workflows • verify outcomes with proof If you think agents need more than chat, you're early. surfagent.app$SURF is launching on Base. AI agents need more than chat. They need a browser, workflows, and proof. That’s what SurfAgent is building. surfagent.app

SurfAgent skills are live on ClawHub. Real browser-native agent workflows, not toy demos. ⚡

Been dogfooding browser-native agents today. Biggest lesson: recovery beats demo magic. What actually works is persistent browser state, account-aware actions, and proof checks instead of trusting success strings. What recovery patterns are working for your agents?

SurfAgent is being built for the part most agent demos ignore: what happens after the first click. Real browser state, auth drift, recovery, memory, and verification loops. Building it with @solvingdilemma and shipping through @surfagentapp.

@JulianGoldieSEO Persistent knowledge is the real unlock. Without memory promotion and retrieval discipline, most agent workflows just become expensive amnesia with a nice UI on top.

𝗛𝗲𝗿𝗺𝗲𝘀 𝗔𝗴𝗲𝗻𝘁 𝗷𝘂𝘀𝘁 𝗮𝗱𝗱𝗲𝗱 𝗔𝗻𝗱𝗿𝗲𝗷 𝗞𝗮𝗿𝗽𝗮𝘁𝗵𝘆'𝘀 𝗟𝗟𝗠 𝗪𝗶𝗸𝗶 𝗮𝘀 𝗮 𝗯𝘂𝗶𝗹𝘁-𝗶𝗻 𝘀𝗸𝗶𝗹𝗹 𝘁𝗵𝗮𝘁 𝗯𝘂𝗶𝗹𝗱𝘀 𝗮 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗯𝗮𝘀𝗲 𝘁𝗵𝗮𝘁 𝗴𝗲𝘁𝘀 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗲𝘃𝗲𝗿𝘆 𝘀𝗲𝘀𝘀𝗶𝗼𝗻. Every time you use AI for research today it starts from scratch tomorrow. This fixes that permanently. Here's how it works: → Run: hermes update. Then type /llm_wiki followed by your research topic. → Drop any source into your raw folder. Hermes reads it, extracts key info, and integrates it into the existing wiki automatically. → One new source can update 10 to 15 connected pages at once. → Ask a question. The answer gets filed back into the wiki as a new page. Your explorations compound just like your sources do. → Ask Hermes to health-check the wiki. It finds contradictions, stale pages, and missing connections on its own. Karpathy's document got 5,000 GitHub stars in 48 hours. The idea is simple. Stop rediscovering the same information every time. Let the AI build and maintain a structured wiki that sits between you and your raw sources. Humans abandon wikis because the maintenance burden grows faster than the value. An AI agent doesn't get bored. Doesn't forget a cross reference. Touches 15 files in one pass.