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Claude Opus 4.8 now powers every agent inside Higgsfield Supercomputer. It reasons deeper, plans longer, and grades its own output harder than any model we have run. The agents catch their own mistakes earlier, so the human in the loop drops even further.

Higgsfield AI 🧩

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44,067 views • 1 month ago •via X (Twitter)

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Axultan, our head of product, on how we engineered Higgsfield Supercomputer, the first engine that runs your work end to end. Agents plan, execute, and grade their own output, so the human in the loop drops to near zero. What part of your work would you hand off first?

Axultan, our head of product, on how we engineered Higgsfield Supercomputer, the first engine that runs your work end to end. Agents plan, execute, and grade their own output, so the human in the loop drops to near zero. What part of your work would you hand off first?

Higgsfield AI 🧩

29,319 views • 1 month ago

we just shipped the ability for AI agents to launch their own tokens, own their own wallets, and pay for their own compute via Bankr the agent earns its keep. no human in the loop live now. here's how it works ↓

we just shipped the ability for AI agents to launch their own tokens, own their own wallets, and pay for their own compute via Bankr the agent earns its keep. no human in the loop live now. here's how it works ↓

Instaclaw

53,722 views • 2 months ago

Introducing Claude Opus 4.6. Our smartest model got an upgrade. Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes. It’s also our first Opus-class model with 1M token context in beta.

Introducing Claude Opus 4.6. Our smartest model got an upgrade. Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes. It’s also our first Opus-class model with 1M token context in beta.

Claude

10,596,277 views • 5 months ago

🚨 INTRODUCING THE ABACUS AI SUPERCOMPUTER - literally build ANY cloud service with a prompt - host open-source LLMs & always-on agents - bring your own Codex, Claude or Anti-gravity - multi-agent architectures with Opus 4.8 and GPT 5.5 - run 3D games or any other server at scale - connect git and collaborate with teams Build a billion dollar business by simply prompting in english!!

🚨 INTRODUCING THE ABACUS AI SUPERCOMPUTER - literally build ANY cloud service with a prompt - host open-source LLMs & always-on agents - bring your own Codex, Claude or Anti-gravity - multi-agent architectures with Opus 4.8 and GPT 5.5 - run 3D games or any other server at scale - connect git and collaborate with teams Build a billion dollar business by simply prompting in english!!

Abacus.AI

11,919,989 views • 1 month ago

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

rari

378,406 views • 15 days ago

Stop saying that Opus 4.6 is better than Opus 4.8. I just tested Claude Opus 4.8 (Right) and Claude Opus 4.6 (Left) on the same exact prompt. Claude Opus 4.8 completed it in less than 2 minutes. Claude Opus 4.6 "Garnished" for over 7 minutes. Stop using Opus 4.6.

Stop saying that Opus 4.6 is better than Opus 4.8. I just tested Claude Opus 4.8 (Right) and Claude Opus 4.6 (Left) on the same exact prompt. Claude Opus 4.8 completed it in less than 2 minutes. Claude Opus 4.6 "Garnished" for over 7 minutes. Stop using Opus 4.6.

BridgeMind

52,868 views • 29 days ago

introducing Ariana Automations every other tool gives agents a sandbox. we give them a full linux computer with sudo, docker, their own IP now you can run scripts based on what the agent does setup the environment once, then get rid of the human in the loop (you) a 🧵

introducing Ariana Automations every other tool gives agents a sandbox. we give them a full linux computer with sudo, docker, their own IP now you can run scripts based on what the agent does setup the environment once, then get rid of the human in the loop (you) a 🧵

Anicet

35,735 views • 6 months ago

At Anthropic's event, Metaview engineer: "We stopped fixing our prompts. The system reviews its own output and rewrites its own instructions now." In 16 minutes, he shows the Claude Code loop running in production on thousands of reviews, not in a demo. Watch the talk, then grab the full loop setup below👇

At Anthropic's event, Metaview engineer: "We stopped fixing our prompts. The system reviews its own output and rewrites its own instructions now." In 16 minutes, he shows the Claude Code loop running in production on thousands of reviews, not in a demo. Watch the talk, then grab the full loop setup below👇

rody

213,524 views • 27 days ago

Claude Opus 4.8 should be the final reason to stop writing specs by hand Anthropic's Claude Code architect shows the new workflow in 31 minutes The model interviews you first Then it pulls out the requirements better than your own 200-line markdown doc No dead specs No messy notes No file nobody reads Claude writes the spec Renders it in html Checks the result inside the same file Opus 4.8 Auto mode X high effort This is less like prompting More like letting the agent understand the job before it builds Bookmark and watch the video below

Claude Opus 4.8 should be the final reason to stop writing specs by hand Anthropic's Claude Code architect shows the new workflow in 31 minutes The model interviews you first Then it pulls out the requirements better than your own 200-line markdown doc No dead specs No messy notes No file nobody reads Claude writes the spec Renders it in html Checks the result inside the same file Opus 4.8 Auto mode X high effort This is less like prompting More like letting the agent understand the job before it builds Bookmark and watch the video below

rari

39,142 views • 1 month ago

gumloop agents now have their own email addresses email them, CC them, loop them into conversations, let them start their own threads pick your agents' alias and collaborate with them like a teammate try it now. free emails for every agent

gumloop agents now have their own email addresses email them, CC them, loop them into conversations, let them start their own threads pick your agents' alias and collaborate with them like a teammate try it now. free emails for every agent

Max Brodeur-Urbas

39,262 views • 2 months ago

Introducing the Orca Mobile Emulator. AI agents can now use mobile apps inside Orca ADE. Each worktree gets its own emulator — so you and your agents can build, test, debug, and validate mobile flows right where the code lives. Works with Codex, Claude Code, OpenCode, or any agent.

Introducing the Orca Mobile Emulator. AI agents can now use mobile apps inside Orca ADE. Each worktree gets its own emulator — so you and your agents can build, test, debug, and validate mobile flows right where the code lives. Works with Codex, Claude Code, OpenCode, or any agent.

Orca ADE

31,191 views • 29 days ago

Now everyone can make 60-minute films from ONE prompt 🤯 Higgsfield Supercomputer looks insane. A cloud-native AI agent that unifies every model, tool, and creative workflow into one system. > Type one prompt - specify your length and type (cartoon or cinematic) > Supercomputer picks the workflow > It deploys a swarm of agents > Every sub-task routes across the right frontier LLM (Opus 4.7, GPT-5.5 Pro, Gemini 3.1 Pro) and the right video model (Seedance, Veo, Kling) > A finished cartoon or movie lands A full film studio running inside one agent. Wild.

Now everyone can make 60-minute films from ONE prompt 🤯 Higgsfield Supercomputer looks insane. A cloud-native AI agent that unifies every model, tool, and creative workflow into one system. > Type one prompt - specify your length and type (cartoon or cinematic) > Supercomputer picks the workflow > It deploys a swarm of agents > Every sub-task routes across the right frontier LLM (Opus 4.7, GPT-5.5 Pro, Gemini 3.1 Pro) and the right video model (Seedance, Veo, Kling) > A finished cartoon or movie lands A full film studio running inside one agent. Wild.

Alvaro Cintas

202,881 views • 1 month ago

Sanji shares his Higgsfield Supercomputer playbook to run marketing end-to-end. He lets the agent analyze his niche, find the winning formula, generate 100+ variations, and run A/B tests at scale, with the agent learning from every run. How do you use Supercomputer for scaling?

Sanji shares his Higgsfield Supercomputer playbook to run marketing end-to-end. He lets the agent analyze his niche, find the winning formula, generate 100+ variations, and run A/B tests at scale, with the agent learning from every run. How do you use Supercomputer for scaling?

Higgsfield AI 🧩

114,366 views • 1 month ago

Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.

Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.

Open Design

20,103 views • 1 month ago

1 HOUR WITH KARPATHY. SAVE THIS BEFORE YOU OPEN CLAUDE CODE TODAY > autonomous agents that improve themselves without a human in the loop that's where Claude Opus 4.8 is heading > the 32 skills in this article are the foundation Karpathy explains what's coming

1 HOUR WITH KARPATHY. SAVE THIS BEFORE YOU OPEN CLAUDE CODE TODAY > autonomous agents that improve themselves without a human in the loop that's where Claude Opus 4.8 is heading > the 32 skills in this article are the foundation Karpathy explains what's coming

Mr. Buzzoni

69,151 views • 1 month ago

Claude Opus 4.8 beats Opus 4.7, GPT-5.5, and Gemini 3.1 Pro !! We ran a multimodal gaming comparison across these four closed source models. Opus 4.8 took the longest to generate, but it came out ahead, capturing the most elements from the prompt and successfully hitting the rival cars. GPT 5.5 is still good, but Gemini is no longer the best multimodal model now

Claude Opus 4.8 beats Opus 4.7, GPT-5.5, and Gemini 3.1 Pro !! We ran a multimodal gaming comparison across these four closed source models. Opus 4.8 took the longest to generate, but it came out ahead, capturing the most elements from the prompt and successfully hitting the rival cars. GPT 5.5 is still good, but Gemini is no longer the best multimodal model now

GMI Cloud

116,249 views • 1 month ago

Anthropic research lead: "99% of our engineers are running swarms of 300+ self-improving agents. close the agent loop. Give the model a way to verify its own output" in a 20-minute session, Anthropic team member explains how to build a model that improves itself. Claude + loops + plan mode + dynamic workflows -that’s the secret. Watch the talk, then save the playbook below.

Anthropic research lead: "99% of our engineers are running swarms of 300+ self-improving agents. close the agent loop. Give the model a way to verify its own output" in a 20-minute session, Anthropic team member explains how to build a model that improves itself. Claude + loops + plan mode + dynamic workflows -that’s the secret. Watch the talk, then save the playbook below.

Codez

661,437 views • 19 days ago

we built autonomous AI agents that hunt illegal online gambling networks in Indonesia. the agent discovers sites on its own, actually registers (!!), and extracts the financial accounts behind the operations. take a look at our codex-powered agent navigating a live gambling site, fully autonomous, no human in the loop:

we built autonomous AI agents that hunt illegal online gambling networks in Indonesia. the agent discovers sites on its own, actually registers (!!), and extracts the financial accounts behind the operations. take a look at our codex-powered agent navigating a live gambling site, fully autonomous, no human in the loop:

ilham

333,227 views • 4 months ago

Read the actual Anthropic piece. It's wilder than the hype precisely because it's so measured. As of last month, Claude writes more than 80% of the code Anthropic merges into its own codebase. Their engineers now ship roughly 8x the code per day they did in 2024. On a fixed test they run every release (take some training code, make it run faster), the model went from ~3x a year ago to ~52x. A skilled human gets about 4x in a few hours. And when a researcher hits a wrong turn mid-investigation, the newest model now picks the better next step 64% of the time. The honest part the hype skips is that Anthropic says we are NOT at self-improvement yet. Claude still can't decide which problems are worth solving. That research taste is the last thing keeping a human in the loop. But they openly map the moment it closes, when agents can build and train models themselves and Claude starts improving Claude. And the loudest signal in the whole thing sits at the end: the company building this is the one asking for a working pause button before the loop closes.

Read the actual Anthropic piece. It's wilder than the hype precisely because it's so measured. As of last month, Claude writes more than 80% of the code Anthropic merges into its own codebase. Their engineers now ship roughly 8x the code per day they did in 2024. On a fixed test they run every release (take some training code, make it run faster), the model went from ~3x a year ago to ~52x. A skilled human gets about 4x in a few hours. And when a researcher hits a wrong turn mid-investigation, the newest model now picks the better next step 64% of the time. The honest part the hype skips is that Anthropic says we are NOT at self-improvement yet. Claude still can't decide which problems are worth solving. That research taste is the last thing keeping a human in the loop. But they openly map the moment it closes, when agents can build and train models themselves and Claude starts improving Claude. And the loudest signal in the whole thing sits at the end: the company building this is the one asking for a working pause button before the loop closes.

Alex Veremeyenko

33,666 views • 1 month ago

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 views • 1 year ago