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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.

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Hasan Toor

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20,754 просмотров • 3 месяцев назад •via X (Twitter)

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Your AI sucks. And it's not the model. It's you. "Make it better" isn't a prompt, it's a guessing game. I changed one step in my process and the output quality completely flipped. Same ideas, same models, totally different results.
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Your AI sucks. And it's not the model. It's you. "Make it better" isn't a prompt, it's a guessing game. I changed one step in my process and the output quality completely flipped. Same ideas, same models, totally different results.

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You can smell a big model. Not the parameter count. Not the benchmark score. It's that feeling when something is actually reasoning. Not just pattern matching. We call it "big model smell."
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You can smell a big model. Not the parameter count. Not the benchmark score. It's that feeling when something is actually reasoning. Not just pattern matching. We call it "big model smell."

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Sub-agent Model Selection — Different Tasks, Different Models Your main agent runs Qwen3.6-Plus for quality. But not every subtask needs a flagship model. Now sub-agents can use a different model. Create a skill file with model: openai:qwen3.5-plus and the sub-agent runs on that model. Powerful model for the hard parts, fast model for the easy parts. Save tokens without sacrificing quality on what matters.
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Sub-agent Model Selection — Different Tasks, Different Models Your main agent runs Qwen3.6-Plus for quality. But not every subtask needs a flagship model. Now sub-agents can use a different model. Create a skill file with model: openai:qwen3.5-plus and the sub-agent runs on that model. Powerful model for the hard parts, fast model for the easy parts. Save tokens without sacrificing quality on what matters.

Qwen

21,333 просмотров • 3 месяцев назад

Hit your usage limit in Codex/Claude Code mid-task? Orca now lets you resume the exact same session in a different agent harness — one click. Codex ⇄ Claude Code, full context intact🔥
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Hit your usage limit in Codex/Claude Code mid-task? Orca now lets you resume the exact same session in a different agent harness — one click. Codex ⇄ Claude Code, full context intact🔥

Orca ADE

48,371 просмотров • 10 дней назад

Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below
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Opus 4.7 - 400k vs 1m context - is there a difference? I've heard Theo - t3.gg talk about the fact that it is unlikely that Anthropic would have offered up a model with 1m context at the same cost, if it wasn't a different (i.e. cheaper to serve) model. I did a test where I toggled the 1m default model on & off in Claude Code (otherwise default settings, xHigh reasoning) and compared the outputs with 3x generations - same prompts etc. My observations: - Models feel DIFFERENT - often when you ask a model for the same generation, you get a somewhat different answer, but it feels & smells the same. Here 400k and 1m are very different every time - 400k model seems better - not that 1m is trash and 400k is amazing, but there are definitely issues with the level of ambition and accuracy that 1m model seems to have Examples of 1m failing: - Voxel Rome: the colosseum is nowhere near as impressive - Golden Gate: cars go sideways, waves not very high, bridge goes into land; though the structure of the bridge is a bit better - Stonehenge: structure is more 'wrong', lighting, shadows & textures are more flat and not as rich This isn't a conclusive evidence of course, but at least to me the two models do not behave the same way. Anecdotally as well when building 1m felt like it was doing more weird validation (e.g. going around in circles) and 400k was more straightforward. These sorts of things are harder to capture in tests, but you'd notice in Claude Code. You can review the hosted generations, see the code & prompts in the links below

Peter Gostev (SF: 22-26 June)

29,203 просмотров • 3 месяцев назад

wow... I just cut my claude code per-call cost to almost nothing with a 30-second config swap. Step 3.5 from StepFun plugs into your existing ~/.claude/settings.json by swapping three env vars. same workflow, different backend underneath. my agents have not stalled once since the switch. full setup below 🧵
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wow... I just cut my claude code per-call cost to almost nothing with a 30-second config swap. Step 3.5 from StepFun plugs into your existing ~/.claude/settings.json by swapping three env vars. same workflow, different backend underneath. my agents have not stalled once since the switch. full setup below 🧵

Farhan

17,871 просмотров • 2 месяцев назад

Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.
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Anthropic pays its ML engineers $500K+/year to deploy what Percy Liang just taught for free in Lecture 3 of CS221. 80 minutes. one whiteboard. everything between "a neuron" and the model running in your IDE right now. three things click after this one: / why two Claude prompts that "mean the same thing" land in different regions of weight space / why fine-tuning sometimes makes a model dumber, not smarter / what RLHF actually changes when Anthropic aligns Claude same $90K Stanford sequence. lecture 3 of ~20. free. the gap between "using Claude" and "understanding it" is exactly one lecture wide.

dunik

24,381 просмотров • 2 месяцев назад

alright agent nerds, if you care about your tokens and usage limits, pay attention to the tools you give to your agents. i built a benchmark that compared various browser tools for agents, and here's an example of their massive difference in cost and latency doing the same task
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alright agent nerds, if you care about your tokens and usage limits, pay attention to the tools you give to your agents. i built a benchmark that compared various browser tools for agents, and here's an example of their massive difference in cost and latency doing the same task

Kun Chen

570,273 просмотров • 3 месяцев назад

Same model psd for both Same rigger Left is 2022 and the one on the Right is 2025 a few days ago It's crazy how much a riggers can really change how a model looks LIKE THAT DOESN'T EVEN LOOK LIKE THE SAME MODEL, BUT IT IS!!
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Same model psd for both Same rigger Left is 2022 and the one on the Right is 2025 a few days ago It's crazy how much a riggers can really change how a model looks LIKE THAT DOESN'T EVEN LOOK LIKE THE SAME MODEL, BUT IT IS!!

💙💉 Khione || Vtuber

86,645 просмотров • 1 год назад

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.
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watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

Sudo su

32,589 просмотров • 1 месяц назад

“The model alone is no longer the product" Aravind Srinivas says the real product is now the harness around it: orchestration, tools, enterprise context, and cost performance. The post-frontier AI race is about systems, not just the smartest model
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“The model alone is no longer the product" Aravind Srinivas says the real product is now the harness around it: orchestration, tools, enterprise context, and cost performance. The post-frontier AI race is about systems, not just the smartest model

Deirdre Bosa

101,960 просмотров • 23 дней назад

It's the same policy as Trudeau, just with a different face.
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It's the same policy as Trudeau, just with a different face.

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Here we see GPT-5.1 struggle to read a 500 lines markdown file for almost 2 minutes. Same model in a different harness does it in a few seconds (takes 2 tool calls for whatever reason..). Is all the Codex-love an elaborate joke I'm not in on?
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Here we see GPT-5.1 struggle to read a 500 lines markdown file for almost 2 minutes. Same model in a different harness does it in a few seconds (takes 2 tool calls for whatever reason..). Is all the Codex-love an elaborate joke I'm not in on?

Mario Zechner

210,131 просмотров • 8 месяцев назад

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.
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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

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Head of Claude Code: "The model changes so often that I sometimes get stuck in this old way of thinking. I find that new people on the team do stuff in a more AGI forward way than I do. You have to transport yourself to the current moment and not get stuck back in an old model, because it's not Sonnet 3.5 anymore. The new models are just completely, completely different."
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Head of Claude Code: "The model changes so often that I sometimes get stuck in this old way of thinking. I find that new people on the team do stuff in a more AGI forward way than I do. You have to transport yourself to the current moment and not get stuck back in an old model, because it's not Sonnet 3.5 anymore. The new models are just completely, completely different."

Lenny Rachitsky

71,551 просмотров • 5 месяцев назад

On Stanford CS336, Tatsu Hashimoto dropped a truth bomb: LLMs aren’t innovating anymore. They’re converging. 2024 → copy LLaMA 2 2025 → don’t let training crash 2026 → win on long context Under the hood? Same stack everywhere: RMSNorm. RoPE. GLUs. GQA. No bias. Different names. Same blueprint. You’re not building a new model— just remixing the same one.
1:29:02

On Stanford CS336, Tatsu Hashimoto dropped a truth bomb: LLMs aren’t innovating anymore. They’re converging. 2024 → copy LLaMA 2 2025 → don’t let training crash 2026 → win on long context Under the hood? Same stack everywhere: RMSNorm. RoPE. GLUs. GQA. No bias. Different names. Same blueprint. You’re not building a new model— just remixing the same one.

Manisha Mishra

58,972 просмотров • 2 месяцев назад

/harness A Claude Code plugin built for Fable 5. It runs your agent against your project, watches it break, and writes a CLAUDE. md from the actual failures. It catches wrong paths, missing scripts, and bad assumptions so your agent never hits the same wall twice ↓
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/harness A Claude Code plugin built for Fable 5. It runs your agent against your project, watches it break, and writes a CLAUDE. md from the actual failures. It catches wrong paths, missing scripts, and bad assumptions so your agent never hits the same wall twice ↓

Hyperbrowser

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Charlie Munger: “The reason economists are wrong is because it’s so hard. …It’s not like physics. The same…recipe done a different time gets a different result.”
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Charlie Munger: “The reason economists are wrong is because it’s so hard. …It’s not like physics. The same…recipe done a different time gets a different result.”

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