Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

not every task needs a frontier model! we built a model router into our coding agent's harness and cut costs by 64% w/o reducing quality. this vid walks through how it works, and how you can build one into your own harness!

sydneyrunkle's profile picture

Sydney Runkle

14,703 subscribers

12,620 görüntüleme • 8 gün önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

3 Yorum

Kaushik Holla profil fotoğrafı
Kaushik Holla8 gün önce

We did something similar in one of our applications. Used a decision model(similar to Jev) to route each task to the right model instead of defaulting everything to a single model. Saw comparable performance while significantly reducing inference cost.

Luis Manrique profil fotoğrafı
Luis Manrique8 gün önce

you don't need a PhD in the mailroom!

Blum profil fotoğrafı
Blum8 gün önce

Running a frontier model on every task is a huge waste of your money

Benzer Videolar

Introducing Smart Routing in Unity AI Gateway. Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance. Match frontier quality and cut task costs by 30%+.
0:37

Introducing Smart Routing in Unity AI Gateway. Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance. Match frontier quality and cut task costs by 30%+.

Databricks

126,674 görüntüleme • 2 ay önce

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below
0:24

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below

Mr. Buzzoni

82,346 görüntüleme • 9 gün önce

Announcing Together Link! Run frontier open models in your favorite coding harness. Track your spending and let our Auto router pick the model for you: low-cost models for quick fixes, more capable models for harder tasks.
0:57

Announcing Together Link! Run frontier open models in your favorite coding harness. Track your spending and let our Auto router pick the model for you: low-cost models for quick fixes, more capable models for harder tasks.

Together AI

29,889 görüntüleme • 9 gün önce

We gave a code harness to a video world model, and built a game you can play on Roblox…
0:54

We gave a code harness to a video world model, and built a game you can play on Roblox…

Alberto Hojel

16,728 görüntüleme • 4 ay önce

🚨 Frontier Model Router - Mix And Match All the Frontier LLMs Our most popular custom router is our frontier-model router that combines the best LLMs for all your tasks GPT 5.6 sol - data analysis Fable / Opus 4.8 - coding Flash 3.5 - summarization Grok 4.5 - real-time Pick your favorite LLMs for all your tasks. As of today the custom routers can be used in ANY harness - Abacus AI, Claude, Codex, Open-Code....
1:21

🚨 Frontier Model Router - Mix And Match All the Frontier LLMs Our most popular custom router is our frontier-model router that combines the best LLMs for all your tasks GPT 5.6 sol - data analysis Fable / Opus 4.8 - coding Flash 3.5 - summarization Grok 4.5 - real-time Pick your favorite LLMs for all your tasks. As of today the custom routers can be used in ANY harness - Abacus AI, Claude, Codex, Open-Code....

Bindu Reddy

4,592,609 görüntüleme • 3 ay önce

Introducing typesafe/jev-router: a cache-aware model router powered by Jev and TypeSafe AI The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻
0:52

Introducing typesafe/jev-router: a cache-aware model router powered by Jev and TypeSafe AI The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻

OpenRouter

1,330,859 görüntüleme • 19 gün önce

Factory benchmarks let you build your own model bench using your past coding agent runs. - Mirrors your environment, secrets, and MCPs for each run - Scores output on judging criteria you define - Generates a report with model recs using cost vs. quality Here's how it works:
6:05

Factory benchmarks let you build your own model bench using your past coding agent runs. - Mirrors your environment, secrets, and MCPs for each run - Scores output on judging criteria you define - Generates a report with model recs using cost vs. quality Here's how it works:

Warp

72,768 görüntüleme • 1 ay önce

Built a 3D Pokémon Go with Replit ⠕ Agent's Gaming stack. From 3D model generation to complete games - all through prompting on Replit. Vibe coding these games feels like a glimpse into the future. Imagine how much kids can learn in vibe coding their own games.
0:54

Built a 3D Pokémon Go with Replit ⠕ Agent's Gaming stack. From 3D model generation to complete games - all through prompting on Replit. Vibe coding these games feels like a glimpse into the future. Imagine how much kids can learn in vibe coding their own games.

Zhen Li

97,820 görüntüleme • 1 yıl önce

Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓
0:24

Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓

Annatar.md

79,652 görüntüleme • 1 ay önce

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness the promise: > 200x faster > 400x cheaper the model hasn't been the slow part for a while the speed and the cost sit in the harness wrapped around it • how to use it > drop this PDF and the article below into Claude Code or Codex > let it rebuild its own setup one evening of setup and your agent starts next week on a harness most teams still don't have 👇
0:24

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness the promise: > 200x faster > 400x cheaper the model hasn't been the slow part for a while the speed and the cost sit in the harness wrapped around it • how to use it > drop this PDF and the article below into Claude Code or Codex > let it rebuild its own setup one evening of setup and your agent starts next week on a harness most teams still don't have 👇

Mr. Buzzoni

128,303 görüntüleme • 19 gün önce

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.
3:19

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 görüntüleme • 3 ay önce

With Intelligent Model Routing, Replit picks the model for every task you send, so you can operate with quality, speed, and efficiency, at no extra cost. Here’s how it works, plus a breakdown on the difference between Free, Power and Max ↓
2:51

With Intelligent Model Routing, Replit picks the model for every task you send, so you can operate with quality, speed, and efficiency, at no extra cost. Here’s how it works, plus a breakdown on the difference between Free, Power and Max ↓

Replit ⠕

11,941 görüntüleme • 1 ay önce

Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent.
2:02

Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent.

Freesolo

29,076 görüntüleme • 2 ay önce

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness what their tests show: > up to 200x faster > up to 400x cheaper the model hasn't been the bottleneck for a while speed and cost both come from the harness you wrap around it • how to use it > give this PDF and the article below to Claude Code or Codex > ask it to rebuild its own setup around Jev • how it turns into a promotion > pick the agent workflow your team pays the most for > rebuild it in one evening > bring the before and after bill to your manager one evening of Jev engineering, and next week you're the one running a harness most teams haven't built yet
0:24

this is pure f*cking gold for anyone running coding agents Jev founder Diogo Amogo wrote a PDF on building a Jev harness what their tests show: > up to 200x faster > up to 400x cheaper the model hasn't been the bottleneck for a while speed and cost both come from the harness you wrap around it • how to use it > give this PDF and the article below to Claude Code or Codex > ask it to rebuild its own setup around Jev • how it turns into a promotion > pick the agent workflow your team pays the most for > rebuild it in one evening > bring the before and after bill to your manager one evening of Jev engineering, and next week you're the one running a harness most teams haven't built yet

Annatar.md

66,594 görüntüleme • 5 gün önce

Someone built a plugin that cuts Claude Code's cost by 54% on the same task, same model. Not a different model. Not a different harness. Just smarter tools underneath. It's called WOZCODE and the benchmark numbers are genuinely CRAZYYYY.
0:30

Someone built a plugin that cuts Claude Code's cost by 54% on the same task, same model. Not a different model. Not a different harness. Just smarter tools underneath. It's called WOZCODE and the benchmark numbers are genuinely CRAZYYYY.

Hasan Toor

20,754 görüntüleme • 6 ay önce

My software factory = agents.md + 5 skills A software factory should be harness / model agnostic and still function the way you want it too Here's how mine works
19:22

My software factory = agents.md + 5 skills A software factory should be harness / model agnostic and still function the way you want it too Here's how mine works

Micky

48,546 görüntüleme • 1 ay önce

Introducing Merge Gateway - Build Your Own Router. You're three sprints into your coding assistant. You pick the most hyped model, integrate, test, deploy. A month later, a new model drops. Now you re-test, re-integrate, re-deploy. Your product didn't change, but the benchmark did. That's how most AI teams operate. Chasing a "best" defined by people who've never seen their product. There is no best model. There's only the right one for your product, users, and use-cases. Build Your Own Router runs on your definition of good. Pick your benchmarks, weigh them, add your own evals. Merge routes every request to your winner. 👉$100 in credits to the first 200 people that comment
1:57

Introducing Merge Gateway - Build Your Own Router. You're three sprints into your coding assistant. You pick the most hyped model, integrate, test, deploy. A month later, a new model drops. Now you re-test, re-integrate, re-deploy. Your product didn't change, but the benchmark did. That's how most AI teams operate. Chasing a "best" defined by people who've never seen their product. There is no best model. There's only the right one for your product, users, and use-cases. Build Your Own Router runs on your definition of good. Pick your benchmarks, weigh them, add your own evals. Merge routes every request to your winner. 👉$100 in credits to the first 200 people that comment

Shensi Ding

3,668,308 görüntüleme • 4 ay önce

Do you have a visualiser in your cupboard? Plug it into your computer and see how you can bring the teaching slides to life through live modelling! 🎬 By using the visualiser, you can model your thinking aloud and children can build alongside you. It also gives you the flexibility to model as many examples as you may need! 🟡🔴
0:57

Do you have a visualiser in your cupboard? Plug it into your computer and see how you can bring the teaching slides to life through live modelling! 🎬 By using the visualiser, you can model your thinking aloud and children can build alongside you. It also gives you the flexibility to model as many examples as you may need! 🟡🔴

White Rose Education

20,974 görüntüleme • 2 yıl önce

This is wild. HeyGen just dropped Avatar IV and it’s INSANE You can now turn your single photo into a video, also captures your voice, tone, and emotion, turning it into hyper-real facial motion. This is the most advanced avatar model ever built Here is how it works:
0:27

This is wild. HeyGen just dropped Avatar IV and it’s INSANE You can now turn your single photo into a video, also captures your voice, tone, and emotion, turning it into hyper-real facial motion. This is the most advanced avatar model ever built Here is how it works:

The AI Colony

89,533 görüntüleme • 1 yıl önce

💡Our view: RSI is a systems engineering problem, not just a model problem. Progress depends on the environment a research agent works in: what resources it can call, what it can change, and how it runs experiments. That environment should reflect real production workflows and be easy for agents to use. At Evolvent AI, we built RSIGym around Everything as a Service. Agents can call established research services and iterate on data, training settings, and harness code. We also introduce RSI-Index to measure how well frontier agents jointly improve a target model’s weights and harness. Across 6 research agents and 5 benchmarks, Opus 5 leads at 0.4809. 🧵 We’re releasing the code, experiment configurations, research trajectories, and evaluation logs. Try your own improvement methods with RSIGym. 🌐 Website: 💻 GitHub: 📄 arXiv:
1:25

💡Our view: RSI is a systems engineering problem, not just a model problem. Progress depends on the environment a research agent works in: what resources it can call, what it can change, and how it runs experiments. That environment should reflect real production workflows and be easy for agents to use. At Evolvent AI, we built RSIGym around Everything as a Service. Agents can call established research services and iterate on data, training settings, and harness code. We also introduce RSI-Index to measure how well frontier agents jointly improve a target model’s weights and harness. Across 6 research agents and 5 benchmarks, Opus 5 leads at 0.4809. 🧵 We’re releasing the code, experiment configurations, research trajectories, and evaluation logs. Try your own improvement methods with RSIGym. 🌐 Website: 💻 GitHub: 📄 arXiv:

Fanqing

250,636 görüntüleme • 7 gün önce