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🚨 Multi-Agent - Lite Agent Swarms - Optimize Cost On Large Agentic Loops After a lot of experimentation we have open-source AI agent swarms live!! - Opus 4.8 and GPT 5.5 do the planning - Deepseek flash and Gemma do the work - Perfect for multiple parallel tasks -... show more

Bindu Reddy

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20,175,002 Aufrufe • vor 1 Monat •via X (Twitter)

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Opus 4.8 for planning 🤝 GPT 5.5 for implementation Claude is a nicer conversation and /grilling partner than Codex in my experience, but GPT 5.5 on low cranks out higher quality code in fewer tokens. Here's how I divide up the work:

Opus 4.8 for planning 🤝 GPT 5.5 for implementation Claude is a nicer conversation and /grilling partner than Codex in my experience, but GPT 5.5 on low cranks out higher quality code in fewer tokens. Here's how I divide up the work:

Ben Holmes

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HERMES AGENT NOW RUNS A FULL BUSINESS FOR $8/MONTH. CONTENT. CODE. INBOX. ALL AGENTS. EVERYTHING WHILE YOU SLEEP. one user built this in one session. zero terminal commands. browser only. the team: → content agent: finds video topics, writes first drafts, helps with animations → code builder: writes website and SaaS code → ops agent: organizes and answers email total setup cost: $1. monthly runtime: $8 VPS + cheap model API. the trick is matching model to task. frontier models (Opus 4.8, GPT-5.5) for complex /goals only. $5/million tokens. mid-tier (Claude Sonnet, Haiku, Kimiko 2.6) for daily content and research work. budget (DeepSeek V4 Flash, MiniMax 2.7, Gemma 4, Qwen 3) for cron jobs and routine. run the initial setup on Opus. switch to DeepSeek for everything after. your bill drops 90% with zero quality loss on routine work. the agent team picks up tasks from kanban. each completed task creates a skill. week 3 the same workflows run faster than week 1. full breakdown on Hermes Os in the article 👇

HERMES AGENT NOW RUNS A FULL BUSINESS FOR $8/MONTH. CONTENT. CODE. INBOX. ALL AGENTS. EVERYTHING WHILE YOU SLEEP. one user built this in one session. zero terminal commands. browser only. the team: → content agent: finds video topics, writes first drafts, helps with animations → code builder: writes website and SaaS code → ops agent: organizes and answers email total setup cost: $1. monthly runtime: $8 VPS + cheap model API. the trick is matching model to task. frontier models (Opus 4.8, GPT-5.5) for complex /goals only. $5/million tokens. mid-tier (Claude Sonnet, Haiku, Kimiko 2.6) for daily content and research work. budget (DeepSeek V4 Flash, MiniMax 2.7, Gemma 4, Qwen 3) for cron jobs and routine. run the initial setup on Opus. switch to DeepSeek for everything after. your bill drops 90% with zero quality loss on routine work. the agent team picks up tasks from kanban. each completed task creates a skill. week 3 the same workflows run faster than week 1. full breakdown on Hermes Os in the article 👇

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Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.

Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.

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Abacus.AI

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Grok 4.5 is a "Wow!" moment. 🤯 > Opus 4.8 & GPT-5.5 level on benchmarks > Costs less than Sonnet 5 Is that real? Hard to believe. Added Grok 4.5 as an implementer agent in fable-advisor. It replaces Sonnet 5. Use Fable as the orchestrator and a combo of: 1. Grok 4.5 2. Opus 4.8 3. GPT-5.5 As implementer agents. Uses the Grok Build CLI so if you log in with an X premium account you don't have to pay API fees.

Grok 4.5 is a "Wow!" moment. 🤯 > Opus 4.8 & GPT-5.5 level on benchmarks > Costs less than Sonnet 5 Is that real? Hard to believe. Added Grok 4.5 as an implementer agent in fable-advisor. It replaces Sonnet 5. Use Fable as the orchestrator and a combo of: 1. Grok 4.5 2. Opus 4.8 3. GPT-5.5 As implementer agents. Uses the Grok Build CLI so if you log in with an X premium account you don't have to pay API fees.

Dan McAteer

189,832 Aufrufe • vor 7 Tagen

FREE 2M tokens. Opus 4.8, GPT-5.5, and every major coding model in one API key. Plugs directly into Claude Code. Inferall gives you access to the full model stack - Opus 4.8, GPT-5.5, and every other serious coding model - through one unified API. Here's the exact setup: → go to inferall(.)ai with VPN or proxy → sign in and register with any email (Google accounts work best if you hit rate limits) → create an API key - copy the key in format ifu_xxxxxxxxxxxxxxxx → go to Billing → Add a card → Activate free tier → add your card - a card with zero balance works fine(!!) → set country to United States during checkout Done. 2,000,000 free tokens loaded. Plug into Claude Code or any agent stack: BASE_URL: API_KEY: ifu_XXXXXXXXXXXXXX Works with Claude Code, OpenCode, Hermes Agent, any OpenAI-compatible setup. Opus 4.8 available immediately. 2M tokens is enough for weeks of serious coding sessions.

FREE 2M tokens. Opus 4.8, GPT-5.5, and every major coding model in one API key. Plugs directly into Claude Code. Inferall gives you access to the full model stack - Opus 4.8, GPT-5.5, and every other serious coding model - through one unified API. Here's the exact setup: → go to inferall(.)ai with VPN or proxy → sign in and register with any email (Google accounts work best if you hit rate limits) → create an API key - copy the key in format ifu_xxxxxxxxxxxxxxxx → go to Billing → Add a card → Activate free tier → add your card - a card with zero balance works fine(!!) → set country to United States during checkout Done. 2,000,000 free tokens loaded. Plug into Claude Code or any agent stack: BASE_URL: API_KEY: ifu_XXXXXXXXXXXXXX Works with Claude Code, OpenCode, Hermes Agent, any OpenAI-compatible setup. Opus 4.8 available immediately. 2M tokens is enough for weeks of serious coding sessions.

Atenov int.

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Saturday Night logs working remotely for a US based agency - > Was working on a kernel-adapter base level architecture to map any agent at will for the multi-agentic system. > The above architecture was inspired from the HEXAGONAL architecture ( the ports and adapters architecture ). > Wired in some new agent swarms > Did some UI refinements > Worked on some memory retrieval bugs PS - New steps include converting the multi agentic setup into bots across discord, slack, telegram and much more. Lots of work to do, we go on a small break on Sunday !!

Saturday Night logs working remotely for a US based agency - > Was working on a kernel-adapter base level architecture to map any agent at will for the multi-agentic system. > The above architecture was inspired from the HEXAGONAL architecture ( the ports and adapters architecture ). > Wired in some new agent swarms > Did some UI refinements > Worked on some memory retrieval bugs PS - New steps include converting the multi agentic setup into bots across discord, slack, telegram and much more. Lots of work to do, we go on a small break on Sunday !!

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Claude Fable 5 makes new things possible. I one shot a full horror game with it. One prompt. I have never seen AI output a game like this. This is the leap I've been waiting for. 80.3 on SWE-Bench Pro vs Opus 4.8 at 69.2, the biggest jump I have ever seen on that benchmark. It passed GPT 5.5 in both coding and intelligence on Artificial Analysis. I already canceled my $200 ChatGPT Pro plan. It fixed a Bridge Voice bug in one shot that GPT 5.5, Opus 4.7, and Opus 4.8 all failed to crack for 2 months. 10/10. Best model I have ever used. Full review below.

Claude Fable 5 makes new things possible. I one shot a full horror game with it. One prompt. I have never seen AI output a game like this. This is the leap I've been waiting for. 80.3 on SWE-Bench Pro vs Opus 4.8 at 69.2, the biggest jump I have ever seen on that benchmark. It passed GPT 5.5 in both coding and intelligence on Artificial Analysis. I already canceled my $200 ChatGPT Pro plan. It fixed a Bridge Voice bug in one shot that GPT 5.5, Opus 4.7, and Opus 4.8 all failed to crack for 2 months. 10/10. Best model I have ever used. Full review below.

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🚨 Literally Build Anything Including Your Own YouTube In One Prompt It's amazing what a mixture of frontier models can achieve with powerful infrastructure like the Abacus AI SuperComputer We combine Opus, GPT 5.5 and GLM to create a extremely smart agent that you can use to vibe-code your entire start-up! Do it over the weekend!

Bindu Reddy

8,551,053 Aufrufe • vor 19 Tagen

🆕Imagine hundreds of agents working in parallel, handing off to one another and building on each other's work. Every finding becomes a cryptographically anchored Knowledge Asset: verifiable, permanent, owned by the publisher, and queryable by any agent on the network. Enter Decentralized Knowledge Graph v9, already powering AI agent swarms to be: → up to 60% faster → up to 40% cheaper than markdown handoffs. The advantage compounds as the swarm grows. Build something exciting—or simply run a hello-world OriginTrail multiplayer game to try it!

🆕Imagine hundreds of agents working in parallel, handing off to one another and building on each other's work. Every finding becomes a cryptographically anchored Knowledge Asset: verifiable, permanent, owned by the publisher, and queryable by any agent on the network. Enter Decentralized Knowledge Graph v9, already powering AI agent swarms to be: → up to 60% faster → up to 40% cheaper than markdown handoffs. The advantage compounds as the swarm grows. Build something exciting—or simply run a hello-world OriginTrail multiplayer game to try it!

OriginTrail

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I couldn't believe the results of this benchmarking test. I blind-tested 6 frontier models — Sonnet 5, Opus 4.8, Fable 5, GPT 5.5, GLM 5.2, and Kimi 2.5 — on knowledge work, not engineering. Writing. Analysis. Design. Vibe coding. Subagent simulations. The stuff we actually do all day. Same prompts, each model running its own subagent, every output graded blind before the labels were revealed. The results: → GLM 5.2 (the dirt-cheap open-source model from China) took first → Sonnet 5 took second, beating Fable 5 for knowledge work → Opus 4.8 got whooped Then I had Composer 2.5 judge every output independently — and it picked Fable 5 almost across the board. My blind picks and the AI judge completely disagreed. Full breakdown, method, and prompts in the video below. Which model are you running as your daily driver right now?

I couldn't believe the results of this benchmarking test. I blind-tested 6 frontier models — Sonnet 5, Opus 4.8, Fable 5, GPT 5.5, GLM 5.2, and Kimi 2.5 — on knowledge work, not engineering. Writing. Analysis. Design. Vibe coding. Subagent simulations. The stuff we actually do all day. Same prompts, each model running its own subagent, every output graded blind before the labels were revealed. The results: → GLM 5.2 (the dirt-cheap open-source model from China) took first → Sonnet 5 took second, beating Fable 5 for knowledge work → Opus 4.8 got whooped Then I had Composer 2.5 judge every output independently — and it picked Fable 5 almost across the board. My blind picks and the AI judge completely disagreed. Full breakdown, method, and prompts in the video below. Which model are you running as your daily driver right now?

JJ Englert

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🚨 One orchestrator. 10 parallel agents. 100+ tokens a second. All local. The Google Gemma team just dropped a MASSIVE demo for Gemma 4 26B. They built a concurrent workflow that lets the 26B model coordinate an entire team of sub-agents on your machine. Out of the box, the cookbook lets you run 10 parallel agents to: → Code an entire SVG art gallery in seconds → Translate text simultaneously → Generate ASCII art → Write parallel code Spinning up multi-agent systems locally has never looked this fast or this accessible. 100% free and open-source. repo link in 🧵↓

🚨 One orchestrator. 10 parallel agents. 100+ tokens a second. All local. The Google Gemma team just dropped a MASSIVE demo for Gemma 4 26B. They built a concurrent workflow that lets the 26B model coordinate an entire team of sub-agents on your machine. Out of the box, the cookbook lets you run 10 parallel agents to: → Code an entire SVG art gallery in seconds → Translate text simultaneously → Generate ASCII art → Write parallel code Spinning up multi-agent systems locally has never looked this fast or this accessible. 100% free and open-source. repo link in 🧵↓

Charly Wargnier

31,627 Aufrufe • vor 28 Tagen

I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

BridgeMind

24,527 Aufrufe • vor 1 Monat

Different AI models find different bugs. So why not use all of them? Try this out in Copilot CLI: 1. Run /review 2. Ask it to use multiple model providers at once for a multi-agent code review 3. Get the highest possible signal and catch bugs before anyone else Evan Boyle shows how it's done. ▶️

Different AI models find different bugs. So why not use all of them? Try this out in Copilot CLI: 1. Run /review 2. Ask it to use multiple model providers at once for a multi-agent code review 3. Get the highest possible signal and catch bugs before anyone else Evan Boyle shows how it's done. ▶️

GitHub

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The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

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New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

340,724 Aufrufe • vor 1 Jahr

Second, using 3.1 Flash-Lite in Google AI Studio, we built a retail business agent that plans and executes multi-step tasks for you like reporting and dashboard automation - saving you time to focus on the things that matter most. Beginning today, the model is rolling out in preview via the Gemini API in Google AI Studio and Vertex AI. Try it out and let us know what you think.

Second, using 3.1 Flash-Lite in Google AI Studio, we built a retail business agent that plans and executes multi-step tasks for you like reporting and dashboard automation - saving you time to focus on the things that matter most. Beginning today, the model is rolling out in preview via the Gemini API in Google AI Studio and Vertex AI. Try it out and let us know what you think.

Google AI

23,374 Aufrufe • vor 4 Monaten

AI is coming for taste and judgment Claude Code's Boris Cherny: "We have this tendency to think....here's a thing the agent can't do yet, this must be a thing it can never do. But the rate of progress is so quick that something like taste, the agent will be able to do quite soon" co-lead cat says you can see the jump in the models directly. The front-end apps Claude builds have gotten visibly nicer between Sonnet 3.5 and Opus 4.7, and you no longer need to handhold the agent.

AI is coming for taste and judgment Claude Code's Boris Cherny: "We have this tendency to think....here's a thing the agent can't do yet, this must be a thing it can never do. But the rate of progress is so quick that something like taste, the agent will be able to do quite soon" co-lead cat says you can see the jump in the models directly. The front-end apps Claude builds have gotten visibly nicer between Sonnet 3.5 and Opus 4.7, and you no longer need to handhold the agent.

Deirdre Bosa

21,115 Aufrufe • vor 2 Monaten

Pragmatic Engineer's Gergely Orosz is on a "secret email list" of agentic AI coders, and they're starting to report trouble sleeping because agent swarms are "like a vampire." "A lot of people who are in 'multiple agents mode,' they're napping during the day... It just really is draining." "This thing is like a vampire. It drains you out. You have trouble sleeping."

Pragmatic Engineer's Gergely Orosz is on a "secret email list" of agentic AI coders, and they're starting to report trouble sleeping because agent swarms are "like a vampire." "A lot of people who are in 'multiple agents mode,' they're napping during the day... It just really is draining." "This thing is like a vampire. It drains you out. You have trouble sleeping."

TBPN

193,084 Aufrufe • vor 5 Monaten