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DeepSeek-V4.1-Flash: the BEST Boeing benchmark result I’ve seen from an open-source model (by far). ✈️ It ran for hours with /goal. Where other models often stall quickly, it kept improving: inspect, spot a defect, zoom in, diagnose, fix, repeat. The video really shows how strong it is at sustaining... show more

Victor M

30,267 subscribers

43,845 görüntüleme • 21 saat önce •via X (Twitter)

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Victor M profil fotoğrafı

Victor M21 saat önce

more details because this is beautiful 🔥

Victor M profil fotoğrafı

Victor M21 saat önce

extremely good, inspect the result here:

Offscript profil fotoğrafı

Offscript21 saat önce

kept improving instead of stalling is the only benchmark that matters now. the rest is one shot theatre

Soft Axiom profil fotoğrafı

Soft Axiom15 saat önce

"Until you are 100% satisfied" might be the most important part of the prompt. The model grading its own work is the benchmark now.

Myravyx profil fotoğrafı

Myravyx18 saat önce

The hard part in a multi-hour run isn't the fixing. It's still being dissatisfied at hour three.

catman profil fotoğrafı

catman19 saat önce

hours of /goal is a much harsher test than a one-shot render. what finally made it stop improving: a visual score, a fixed iteration limit, or its own judgment?

Umar Nasser profil fotoğrafı

Umar Nasser15 saat önce

It seems better than opus 4.8 one right?

Shesaidmewakeup profil fotoğrafı

Shesaidmewakeup16 saat önce

Open source running for hours in claude code and still improving. Benches don’t show that.

John Rood profil fotoğrafı

John Rood19 saat önce

the thing that separates "keeps improving" from "stalls" isn't raw capability. it's whether the model can still see what it already tried 40 steps back. most stalls are it relitigating a fix it already ruled out because that context fell out of the window.

neal profil fotoğrafı

neal20 saat önce

The impressive part is the loop, not the “god model” label: inspect → name a defect → fix → verify. That pattern is useful outside 3D work too—give any model explicit checks and a stop condition instead of asking for a one-shot answer.

OmniWired profil fotoğrafı

OmniWired16 saat önce

I wonder if it be better on dsh

Surya Rajendhran profil fotoğrafı

Surya Rajendhran21 saat önce

this is a great benchmark, where can I find more details? are there any common trends you’re seeing with how deepseek or open weight models handle 3D vs closed frontier models?

Victor M profil fotoğrafı

Victor M20 saat önce

best way to see other results:

Chizzydigital profil fotoğrafı

Chizzydigital17 saat önce

Next time just use a separate critic agent

veloX profil fotoğrafı

veloX17 saat önce

this Boeing benchmark reminds me of the Red Queen Gödel Machine: continuous self-improvement against a moving target.

bbzz profil fotoğrafı

bbzz20 saat önce

what'd it cost?

Elledyne Labs profil fotoğrafı

Elledyne Labs17 saat önce

was the inspect→fix loop running on the same context window the whole time, or did /goal spawn fresh subagents? curious what context strategy kept it from rotting across hours

Krish Dasgupta profil fotoğrafı

Krish Dasgupta18 saat önce

What really is marvellous- the precision shadow. The moment you move the pane around the plane, the direction also specifies the shadow. That’s something !

Wei佳 profil fotoğrafı

Wei佳14 saat önce

What Harness tool are you using?

Elledyne Labs profil fotoğrafı

Elledyne Labs19 saat önce

the harness did the work, not the model. claude code gave it the loop and deepseek just refused to quit. how much of this is /goal's verifier vs deepseek's stamina?

اسلیم کلیک ها profil fotoğrafı

اسلیم کلیک ها20 saat önce

Can you please crash it into a building next

Onur Solmaz profil fotoğrafı

Onur Solmaz15 saat önce

did it reach the final version faster than astra before?

Steffan profil fotoğrafı

Steffan20 saat önce

Looks like I am going to change harness, I was not really happy with deepseek results in opencode, maybe it’s a harness problem.

Victor M profil fotoğrafı

Victor M15 saat önce

try it and tell me - I get really good results with CC with this setup:

FoodTruck Bench profil fotoğrafı

FoodTruck Bench15 saat önce

DeepSeek 4.1 being this close to GPT-5.5 at this price point is actually illegal 💀

josip profil fotoğrafı

josip16 saat önce

the impressive part isn't the boeing render, it's that it stayed in the inspect→diagnose→fix loop for hours instead of stalling after the first pretty output. long-horizon models start looking useful when the harness keeps them pointed at the next defect

Dev profil fotoğrafı

Dev14 saat önce

bro chinese models r literally cooking the entire ai market rn distillation really leveled up chinese ai to the point it's outperforming everything ngl 😭🔥

kimodo_cloud profil fotoğrafı

kimodo_cloud20 saat önce

cost ?

Preyforge profil fotoğrafı

Preyforge16 saat önce

this is the satisfying version of a loop: inspect, diagnose, fix. i still make the deployed path prove the demo; screenshots can be charming little liars.

neo hodler profil fotoğrafı

neo hodler16 saat önce

Interesting to see that it “ran for hours with /goal” without stalling. Long-horizon loops are where most “SOTA” models still quietly die.

Ibesh profil fotoğrafı

Ibesh16 saat önce

boeing test is brutal lol. most open models stall after a few fixes, the fact it kept going for hours and kept improving is the real flex

keethesh profil fotoğrafı

keethesh17 saat önce

Does it ever break something that was already working?

东倒西六条 profil fotoğrafı

东倒西六条18 saat önce

你炒股吗？如果你炒股的话马上删了，否则对今晚美国股市有冲击！能设计外形，就可以对飞机内部进行设计。直接冲击了美国高端制造业

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Old way of using Claude Code: Prompt → wait → review → prompt → wait → review → repeat 20 times. You are the loop. You are the condition check. You are the reason it keeps going. New way: /goal all tests pass and lint is clean Claude works. You do something else. You come back to a result. Here's exactly how to set it up — and how to write conditions that actually work ↓

Old way of using Claude Code: Prompt → wait → review → prompt → wait → review → repeat 20 times. You are the loop. You are the condition check. You are the reason it keeps going. New way: /goal all tests pass and lint is clean Claude works. You do something else. You come back to a result. Here's exactly how to set it up — and how to write conditions that actually work ↓

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If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications. If you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral. Skills you'll gain: - How to build web apps with AI - zero coding skills needed - How to fix and improve your creations by chatting with AI - A simple process you can use to build other things you can dream up Building with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're an experienced engineer, please share this with someone in your life who's been curious about building with AI. Come build with me!

Andrew Ng

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Before Fable got released (and pulled) Mozilla was quietly testing Claude Mythos against Firefox's 10M line codebase. The result? Over 400 security bugs fixes, including ones that had been hiding in the codebase for over a decade. Brian Grinstead, distinguished engineer at Mozilla, walked me through the agentic bug-finding harness behind the model. His take? It was 50% mythos / 50% setup. In this ep, Brian walks through: - why you can't just point a model at 10M lines of code - how to write a good goal/loop pattern - killing false positives with a verifier - why it's good to "lie to the agent" And guess what? This isn't magic - you can write your own similar harness in less than an afternoon. Watch now on YT:

claire vo 🖤

260,235 görüntüleme • 2 ay önce

deepseek v4.1 flash ran all four three.js scenes for $0.15. gemini 3.8 flash needed $1.25 we put the two models on one job: a self-contained html page that renders an animated 3d scene with three.js – a storm at sea, a spiral galaxy, a tornado over farmland, a tesla coil on a dark table. same prompt - calls until all four scenes were usable #1 deepseek v4.1 flash – 6 #2 gemini 3.8 flash – 12 - time #1 deepseek v4.1 flash – 13m 57s #2 gemini 3.8 flash – 32m 02s - cost #1 deepseek v4.1 flash – $0.15 #2 gemini 3.8 flash – $1.25 observations: • gemini blew out the exposure on 4 of 5 first attempts – a white blob where the tesla coil should be. the prompt had a paragraph on exposure. it did not help. • both models overcorrect in the fix loop: "too bright" becomes "almost black". round two is where it lands. same four scenes, 6 calls and $0.15 against 12 calls and $1.25!

deepseek v4.1 flash ran all four three.js scenes for $0.15. gemini 3.8 flash needed $1.25 we put the two models on one job: a self-contained html page that renders an animated 3d scene with three.js – a storm at sea, a spiral galaxy, a tornado over farmland, a tesla coil on a dark table. same prompt - calls until all four scenes were usable #1 deepseek v4.1 flash – 6 #2 gemini 3.8 flash – 12 - time #1 deepseek v4.1 flash – 13m 57s #2 gemini 3.8 flash – 32m 02s - cost #1 deepseek v4.1 flash – $0.15 #2 gemini 3.8 flash – $1.25 observations: • gemini blew out the exposure on 4 of 5 first attempts – a white blob where the tesla coil should be. the prompt had a paragraph on exposure. it did not help. • both models overcorrect in the fix loop: "too bright" becomes "almost black". round two is where it lands. same four scenes, 6 calls and $0.15 against 12 calls and $1.25!

thehype.

60,026 görüntüleme • 1 gün önce

Sam Altman made the case for open-source harnesses in July. a month later, someone shipped it, and it's more efficient than most managed harnesses. here is the problem it was aimed at: a large share of your agent's token bill is the model rereading things it already read. that isn't the model's doing. the runtime around it decides what goes into every prompt and how often the model gets called. for example, an agent queries a CRM at step four and gets back 400 rows. those rows get piled up in the conversation history. by step nineteen, the model has to read those rows fifteen times unnecessarily, and every token read is billed at input rates. it happened because your harness assembled that prompt on every turn and kept the rows in it. that gives you two levers: how much context the harness carries forward, and how often it calls the model. there are four practical ways to keep the prompt from growing unnecessarily: → load tool schemas on demand. a server with 100 tools doesn't need to put all 100 into every prompt when the agent only calls two. → offload large results to disk. turn a large response into a short preview and a file path instead of replaying the entire result on every turn. → delegate to subagents. let a subagent spend thirty tool calls in its own context and return one summary to the root agent. → run toolchains in code. one script calls three tools, joins the results, and returns a table instead of three turns each dragging a full response. but reducing context is only half the job. you also need to control how often the model gets called. a good harness should avoid unnecessary planning, verification, and reflection when the work can be completed in fewer steps. TrueFoundry's open-source agent harness, TrueForge, is built around both of those controls. it sits between the model and the tools, deciding what goes into every prompt and when another model call is actually needed. it also breaks token usage down across the harness, skills, instructions, tools, and messages. DevRev's Enterprise-Bench is where this gets tested, on multi-step tasks of the kind where an agent pulls records from one system and reconciles them against another. TrueFoundry ran TrueForge there against Claude Managed Agents, both on the same model, and both finished the same number of tasks. the tie is the part that matters, because it means the gap underneath is not a quality tradeoff. TrueForge reached that score on close to a third of the tokens, with roughly 40% fewer trips back to the model. for the same result, that comes out around 2.7x cheaper than Claude Managed Agents. swapping in an open model made it sharper still. TrueForge with GLM-5.2 scored a little higher than either setup above, and the entire benchmark run cost about $3 at list prices. being open source matters beyond the license here. the model underneath can be swapped without rewriting the agent, and the whole thing can run inside your own environment when the data cannot leave it. all of this comes down to the runtime around the model, the context it carries, the tools it exposes, and how many times it goes back to the model. that is what a production harness actually owns. the full task list, the per-run numbers, and the MIT-licensed code are on GitHub: (don't forget to star 🌟) you can read more about the same in the article quoted below. thanks to the TrueForge team for working with me on this one.

Sam Altman made the case for open-source harnesses in July. a month later, someone shipped it, and it's more efficient than most managed harnesses. here is the problem it was aimed at: a large share of your agent's token bill is the model rereading things it already read. that isn't the model's doing. the runtime around it decides what goes into every prompt and how often the model gets called. for example, an agent queries a CRM at step four and gets back 400 rows. those rows get piled up in the conversation history. by step nineteen, the model has to read those rows fifteen times unnecessarily, and every token read is billed at input rates. it happened because your harness assembled that prompt on every turn and kept the rows in it. that gives you two levers: how much context the harness carries forward, and how often it calls the model. there are four practical ways to keep the prompt from growing unnecessarily: → load tool schemas on demand. a server with 100 tools doesn't need to put all 100 into every prompt when the agent only calls two. → offload large results to disk. turn a large response into a short preview and a file path instead of replaying the entire result on every turn. → delegate to subagents. let a subagent spend thirty tool calls in its own context and return one summary to the root agent. → run toolchains in code. one script calls three tools, joins the results, and returns a table instead of three turns each dragging a full response. but reducing context is only half the job. you also need to control how often the model gets called. a good harness should avoid unnecessary planning, verification, and reflection when the work can be completed in fewer steps. TrueFoundry's open-source agent harness, TrueForge, is built around both of those controls. it sits between the model and the tools, deciding what goes into every prompt and when another model call is actually needed. it also breaks token usage down across the harness, skills, instructions, tools, and messages. DevRev's Enterprise-Bench is where this gets tested, on multi-step tasks of the kind where an agent pulls records from one system and reconciles them against another. TrueFoundry ran TrueForge there against Claude Managed Agents, both on the same model, and both finished the same number of tasks. the tie is the part that matters, because it means the gap underneath is not a quality tradeoff. TrueForge reached that score on close to a third of the tokens, with roughly 40% fewer trips back to the model. for the same result, that comes out around 2.7x cheaper than Claude Managed Agents. swapping in an open model made it sharper still. TrueForge with GLM-5.2 scored a little higher than either setup above, and the entire benchmark run cost about $3 at list prices. being open source matters beyond the license here. the model underneath can be swapped without rewriting the agent, and the whole thing can run inside your own environment when the data cannot leave it. all of this comes down to the runtime around the model, the context it carries, the tools it exposes, and how many times it goes back to the model. that is what a production harness actually owns. the full task list, the per-run numbers, and the MIT-licensed code are on GitHub: (don't forget to star 🌟) you can read more about the same in the article quoted below. thanks to the TrueForge team for working with me on this one.

Akshay 🚀

76,552 görüntüleme • 23 gün önce

72 hours in and GPT-5.6 Sol is still running without a single follow-up prompt. The task was to build the most comprehensive map possible of the physical infrastructure powering AI, submarine cables, data centers, power plants, substations and electricity grids. The only thing given was a simple loop: → Find the biggest gap → Research it → Build it → Test it → Publish it → Repeat Most people expected this to stall after a few hours. It didn't. The Mac restarted mid-run and Codex picked up exactly where it left off without losing anything. What this actually proves is that the prompt matters less than the goal. The smarter the model, the more room you need to give it to make its own decisions. Constrain it with steps and you get a worse version of what it could do on its own.

72 hours in and GPT-5.6 Sol is still running without a single follow-up prompt. The task was to build the most comprehensive map possible of the physical infrastructure powering AI, submarine cables, data centers, power plants, substations and electricity grids. The only thing given was a simple loop: → Find the biggest gap → Research it → Build it → Test it → Publish it → Repeat Most people expected this to stall after a few hours. It didn't. The Mac restarted mid-run and Codex picked up exactly where it left off without losing anything. What this actually proves is that the prompt matters less than the goal. The smarter the model, the more room you need to give it to make its own decisions. Constrain it with steps and you get a worse version of what it could do on its own.

0xMarioNawfal

61,751 görüntüleme • 1 ay önce

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

66,150 görüntüleme • 7 gün önce