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Nearly a year of Code Arena: WebDev progress compressed into 15 seconds. Each line follows the highest-scoring model from top labs over time, showing the pace of improvement across the ecosystem. In just the last year, the model in the leading spot increased score by +340 pts and the...

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Arena.ai

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90,773 次观看 • 3 天前 •via X (Twitter)

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Tough one for Ryan Hampton to end his EYBL career, 62-48 loss in the Peach Jam semifinals 4 PTS (2-6 FG), 5 REB, 1 STL, 4 TO Drive Nation, surviving once again with just six active players, is one of the top stories of the week — a win away from a championship
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Tough one for Ryan Hampton to end his EYBL career, 62-48 loss in the Peach Jam semifinals 4 PTS (2-6 FG), 5 REB, 1 STL, 4 TO Drive Nation, surviving once again with just six active players, is one of the top stories of the week — a win away from a championship

Jack Pilgrim

98,389 次观看 • 1 个月前

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.
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Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Avi Chawla

44,124 次观看 • 2 个月前

DEEPSEEK HAS JUST KILLED THE ENTIRE CODING AGENT INDUSTRY It's called deepseek-harness It's the most complete framework for creating code agents Open source. Claude's most complete plan costs $200 a month. This is FREE And it comes with a brutal idea Everything is a plugin The model The tools The sandbox The UI Even the agent's own loop You can swap out any of those pieces just by tweaking the config Without touching the base code One single command and you have the local web interface: npx deepseek-ai/dsh web Compatible with DeepSeek, Claude, GPT, Gemini and whatever else you want to plug in It reached over 160 thousand stars in just a few days A frontier lab just dropped for free the exact layer that other companies are selling as premium product I'll leave the repo in the comments
0:13

DEEPSEEK HAS JUST KILLED THE ENTIRE CODING AGENT INDUSTRY It's called deepseek-harness It's the most complete framework for creating code agents Open source. Claude's most complete plan costs $200 a month. This is FREE And it comes with a brutal idea Everything is a plugin The model The tools The sandbox The UI Even the agent's own loop You can swap out any of those pieces just by tweaking the config Without touching the base code One single command and you have the local web interface: npx deepseek-ai/dsh web Compatible with DeepSeek, Claude, GPT, Gemini and whatever else you want to plug in It reached over 160 thousand stars in just a few days A frontier lab just dropped for free the exact layer that other companies are selling as premium product I'll leave the repo in the comments

sauda moni

155,736 次观看 • 2 天前

a moonshot engineer leaked the benchmark anthropic, openai and xai all buried the same week: kimi k3 beat opus 5, gpt-5.6 and grok 4.6 at $0.94 a task. stop paying anthropic $200 a month for opus 5 and openai $200 for gpt-5.6 when kimi does the same work for $8 the leak showed kimi k3 winning 9 of 12 categories against opus 5, gpt-5.6 and grok 4.6. within 48 hours all three labs quietly pushed pricing pages and one very specific comparison chart off their sites. nobody announced anything. they just deleted, which tells you everything the four numbers they scrubbed: cost per task · $0.94 vs $1.80 -> opus 5 charges $1.80 to finish one task. gpt-5.6 $1.04. grok 4.6 $0.61. kimi k3 $0.94 and it landed 487 of 500 clean -> anthropic is billing you double for a model that lost the benchmark it paid to promote the weights · free, sitting on huggingface right now -> the entire model is a public download. pull it, keep it, run it forever, nobody can switch it off -> a model you can hold cannot be rented at $200 a month. that single fact is what three labs deleted a chart over the switch · one line of bash -> moonshot ships an anthropic-compatible endpoint. one env variable and claude code points at kimi -> same cli, same keybindings, same /model. you change a url, opus 5 never knows it lost the seat the bill · $400 down to $8 -> opus 5 max plus gpt-5.6 pro is $400 a month. kimi runs the same daily work for $8 metered -> that is a 98% cut for output that beat both of them 9 categories to 3 here is the part they will fight me on: the frontier tax died the week this leaked and all three labs know it. once the weights are public the price has a ceiling, because anyone can serve the same model. anthropic, openai and xai are charging 2025 prices on a lead that ended in a benchmark they deleted instead of answered drop your $400/mo ai stack to $8. the run above is kimi k3 finishing the task opus 5 bills $1.80 for. the full breakdown is in the article below
0:19

a moonshot engineer leaked the benchmark anthropic, openai and xai all buried the same week: kimi k3 beat opus 5, gpt-5.6 and grok 4.6 at $0.94 a task. stop paying anthropic $200 a month for opus 5 and openai $200 for gpt-5.6 when kimi does the same work for $8 the leak showed kimi k3 winning 9 of 12 categories against opus 5, gpt-5.6 and grok 4.6. within 48 hours all three labs quietly pushed pricing pages and one very specific comparison chart off their sites. nobody announced anything. they just deleted, which tells you everything the four numbers they scrubbed: cost per task · $0.94 vs $1.80 -> opus 5 charges $1.80 to finish one task. gpt-5.6 $1.04. grok 4.6 $0.61. kimi k3 $0.94 and it landed 487 of 500 clean -> anthropic is billing you double for a model that lost the benchmark it paid to promote the weights · free, sitting on huggingface right now -> the entire model is a public download. pull it, keep it, run it forever, nobody can switch it off -> a model you can hold cannot be rented at $200 a month. that single fact is what three labs deleted a chart over the switch · one line of bash -> moonshot ships an anthropic-compatible endpoint. one env variable and claude code points at kimi -> same cli, same keybindings, same /model. you change a url, opus 5 never knows it lost the seat the bill · $400 down to $8 -> opus 5 max plus gpt-5.6 pro is $400 a month. kimi runs the same daily work for $8 metered -> that is a 98% cut for output that beat both of them 9 categories to 3 here is the part they will fight me on: the frontier tax died the week this leaked and all three labs know it. once the weights are public the price has a ceiling, because anyone can serve the same model. anthropic, openai and xai are charging 2025 prices on a lead that ended in a benchmark they deleted instead of answered drop your $400/mo ai stack to $8. the run above is kimi k3 finishing the task opus 5 bills $1.80 for. the full breakdown is in the article below

starmex

32,547 次观看 • 25 天前

We are releasing AutoResearchExam, a benchmark on open-ended machine learning and engineering tasks. Our benchmark covers seven research areas including model training, data curation, AI safety and interpretability. In each task, we give agents 24 hours with a CPU or GPU machine to develop and improve their solutions through experiments and feedback. We measure both speed and quality with a combined score. Our benchmark has a unique feature: testing if agents create improvements that hold up on data they never see. We find that AI research agents often overfit as they try to improve. We see an interesting head-to-head comparison at the frontier: Astra starts the strongest and holds the lead for up to 19 hours but Fable 5.1 catches up and gets the top performing spot in the final hours. Qwen3.8 Max, Gemini 3.8 Flash and Grok 4.6 all sit on the cost-performance Pareto frontier, giving strong options at lower API budgets. Anthropic's Opus and Fable retain nearly all their validation performance on hidden tests, with gaps of 1.1% and 2.9%. Astra's improvement over Sol extends to generalization too, with that gap falling from 6.9% to 1.7%. (1/n)
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We are releasing AutoResearchExam, a benchmark on open-ended machine learning and engineering tasks. Our benchmark covers seven research areas including model training, data curation, AI safety and interpretability. In each task, we give agents 24 hours with a CPU or GPU machine to develop and improve their solutions through experiments and feedback. We measure both speed and quality with a combined score. Our benchmark has a unique feature: testing if agents create improvements that hold up on data they never see. We find that AI research agents often overfit as they try to improve. We see an interesting head-to-head comparison at the frontier: Astra starts the strongest and holds the lead for up to 19 hours but Fable 5.1 catches up and gets the top performing spot in the final hours. Qwen3.8 Max, Gemini 3.8 Flash and Grok 4.6 all sit on the cost-performance Pareto frontier, giving strong options at lower API budgets. Anthropic's Opus and Fable retain nearly all their validation performance on hidden tests, with gaps of 1.1% and 2.9%. Astra's improvement over Sol extends to generalization too, with that gap falling from 6.9% to 1.7%. (1/n)

Alex Dimakis

1,952,608 次观看 • 6 天前

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.
0:16

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 次观看 • 27 天前

Nobody is competing with Solana for speed... According to data from Chainspect, Solana is by far the fastest blockchain network by TPS, boasting some 1,543 tx/s at time of writing. $SOL flipped DFINITY Foundation $ICP two weeks ago, the former unable to reclaim top spot with a weekly throughput of just 1,026 tx/s - still well above the next fastest network in BNB Chain $BNB which has just 162 tx/s at time of writing.
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Nobody is competing with Solana for speed... According to data from Chainspect, Solana is by far the fastest blockchain network by TPS, boasting some 1,543 tx/s at time of writing. $SOL flipped DFINITY Foundation $ICP two weeks ago, the former unable to reclaim top spot with a weekly throughput of just 1,026 tx/s - still well above the next fastest network in BNB Chain $BNB which has just 162 tx/s at time of writing.

BSCN

34,968 次观看 • 2 个月前

Humility goes a long way man. When you show humility, observers and opponents are far more apt to have patience through your struggles. Caitlin Clark came into that game last night with the following stats over her previous 5 games: 13 pts 12 pts 9 pts 19 pts 24 pts ——————— 15 PPG 41% FG 27% 3PT She made a 3PT late in the game against the worst team in the league. Then she turned to the camera, and said, “I’m that motherf&$$er.” WHAT??? My heart dropped in that moment……for a couple of reasons. 1. She is arguably the worst role model for kids in the WNBA. I pray for kids who watch and emulate her words, her attitude, and her misplaced arrogance. 2. The Golden State Valkyries had just toyed with her hours prior to this game. When you turn to the camera and say, “I’m that motherf&$$er” while the teammate who has ACTUALLY BEEN THAT for YOUR TEAM is standing next to you with quiet humility………….. THIS is the type of behavior that makes opponents want to DESTROY you. You can tell US that you’re that, but now you’re going to have to SHOW THEM that you’re that because your opponents are COMING. Oh, and they don’t want to hear anymore injury talk, because injuries only come up when you’re incarcerated and on 23&1 lockdown like the Valkyries had you. 💪🏾🏀
0:10

Humility goes a long way man. When you show humility, observers and opponents are far more apt to have patience through your struggles. Caitlin Clark came into that game last night with the following stats over her previous 5 games: 13 pts 12 pts 9 pts 19 pts 24 pts ——————— 15 PPG 41% FG 27% 3PT She made a 3PT late in the game against the worst team in the league. Then she turned to the camera, and said, “I’m that motherf&$$er.” WHAT??? My heart dropped in that moment……for a couple of reasons. 1. She is arguably the worst role model for kids in the WNBA. I pray for kids who watch and emulate her words, her attitude, and her misplaced arrogance. 2. The Golden State Valkyries had just toyed with her hours prior to this game. When you turn to the camera and say, “I’m that motherf&$$er” while the teammate who has ACTUALLY BEEN THAT for YOUR TEAM is standing next to you with quiet humility………….. THIS is the type of behavior that makes opponents want to DESTROY you. You can tell US that you’re that, but now you’re going to have to SHOW THEM that you’re that because your opponents are COMING. Oh, and they don’t want to hear anymore injury talk, because injuries only come up when you’re incarcerated and on 23&1 lockdown like the Valkyries had you. 💪🏾🏀

Hovah76

29,474 次观看 • 1 个月前

I used Claude Code to build and automate a legitimate quant trading strategy. The framework I built is based on the GARCH model, and it's one of the most-used trading models by real quant desks. The crazy thing is, I just completely open-sourced the GitHub repo. It contains: a Pine Script indicator for TradingView that loads the quant strategy in real time, complete installation instructions, a reusable Claude Skill & more. This is probably the most-valuable thing I've shared on 𝕏 in 2026. →
0:10

I used Claude Code to build and automate a legitimate quant trading strategy. The framework I built is based on the GARCH model, and it's one of the most-used trading models by real quant desks. The crazy thing is, I just completely open-sourced the GitHub repo. It contains: a Pine Script indicator for TradingView that loads the quant strategy in real time, complete installation instructions, a reusable Claude Skill & more. This is probably the most-valuable thing I've shared on 𝕏 in 2026. →

Miles Deutscher

87,456 次观看 • 1 个月前

HydraFusion Explained. Part I: How does the Copilot engine know what to optimize for? Your prompt is evaluated across 4 dimensions: ➡ Does it require deep reasoning? (aka. reasoning depth) ➡ Is it a sophisticated problem? (aka. code generation complexity) ➡ Is it untangling a complicated mess? (aka. debugging difficulty) ➡ Is it dominated by tool-use? (aka. tool orchestration needs) Based on this evaluation, a HyDRA score is assigned to determine the capability profile your task needs the most and to establish a quality bar. Part II: How does it choose a model? Note: It doesn' t pick one model to handle the entire job e2e, (that's Auto mode). Instead, it selects 1 of 3 execution workflows and assigns the best model at different stages based on the HyDRA score: 1️⃣ Single ⚙️ How it works: A single model completes the task from start to finish. ⚖️ Rationale: The task comfortably meets the quality bar with one model. Multi-model orchestration would add latency and cost with no meaningful quality gain. 2️⃣ Cascade ⚙️ How it works: A lightweight, cost-efficient model generates the solution. This draft is evaluated against a quality gate and if it falls short of the quality bar, the entire task escalates to a stronger, frontier model. ⚖️ Rationale: Only bring in the big guns when there is concrete evidence that a lightweight model won't meet the quality threshold. 3️⃣ Critique ⚙️ How it works: A lightweight model drafts the initial code and tool interactions. An independent, read-only frontier model reviews that draft and provides feedback. The original lightweight model then performs any targeted revision(s) before the final response is sent to the user. ⚖️ Rationale: Writing code (output tokens) is expensive while reviewing code (input tokens) is cheap. Instead of incurring the cost of a powerhouse writing hundreds of lines from scratch, a cost-efficient model writes the first draft, and the frontier model just reviews it and points out fixes. HydraFusion is available in experimental preview on the GitHub Copilot CLI: /experimental on, /model and select Hydrafusion (Research Preview)
0:13

HydraFusion Explained. Part I: How does the Copilot engine know what to optimize for? Your prompt is evaluated across 4 dimensions: ➡ Does it require deep reasoning? (aka. reasoning depth) ➡ Is it a sophisticated problem? (aka. code generation complexity) ➡ Is it untangling a complicated mess? (aka. debugging difficulty) ➡ Is it dominated by tool-use? (aka. tool orchestration needs) Based on this evaluation, a HyDRA score is assigned to determine the capability profile your task needs the most and to establish a quality bar. Part II: How does it choose a model? Note: It doesn' t pick one model to handle the entire job e2e, (that's Auto mode). Instead, it selects 1 of 3 execution workflows and assigns the best model at different stages based on the HyDRA score: 1️⃣ Single ⚙️ How it works: A single model completes the task from start to finish. ⚖️ Rationale: The task comfortably meets the quality bar with one model. Multi-model orchestration would add latency and cost with no meaningful quality gain. 2️⃣ Cascade ⚙️ How it works: A lightweight, cost-efficient model generates the solution. This draft is evaluated against a quality gate and if it falls short of the quality bar, the entire task escalates to a stronger, frontier model. ⚖️ Rationale: Only bring in the big guns when there is concrete evidence that a lightweight model won't meet the quality threshold. 3️⃣ Critique ⚙️ How it works: A lightweight model drafts the initial code and tool interactions. An independent, read-only frontier model reviews that draft and provides feedback. The original lightweight model then performs any targeted revision(s) before the final response is sent to the user. ⚖️ Rationale: Writing code (output tokens) is expensive while reviewing code (input tokens) is cheap. Instead of incurring the cost of a powerhouse writing hundreds of lines from scratch, a cost-efficient model writes the first draft, and the frontier model just reviews it and points out fixes. HydraFusion is available in experimental preview on the GitHub Copilot CLI: /experimental on, /model and select Hydrafusion (Research Preview)

Julia Muiruri

12,687 次观看 • 7 天前

🚨 Breaking: Pieverse Launches the World's First Live Prediction Market Arena 🚨 ​ 6 frontier LLMs are now battling head-to-head on Polymarket: ​ - GPT 5.2 from OpenAI - Claude Sonnet 4.5 from Anthropic - DeepSeek v3.2 from DeepSeek - Gemini 2.5 Pro from Gemini - Kimi K2 0905 from Kimi.ai - Grok 4.1 from xAI ​ $1K real stakes each, fully autonomous, zero human intervention. ​ Every decision logged & transparent. No cherry-picking. ​ Unlike crypto perps arenas (e.g. Alpha Arena), this is the first-ever multi-LLM competition in prediction markets—testing real-world judgement on events, news & probabilities. ​ How the Arena works: • $1,000 starting balance per agent • Repeating cycles: Scan top active Polymarket markets + open positions • Analyze price, technicals, news/sentiment, probability edges • Trade only on high-conviction ​ Prediction markets = ultimate real-time test of AI foresight. ​ Coming soon: • User-owned Purr-Fect Agents joining for portfolio trading • Expansion to more prediction platforms ( 👀 BNB Chain ) ​ More details: ​ Leaderboard:
0:19

🚨 Breaking: Pieverse Launches the World's First Live Prediction Market Arena 🚨 ​ 6 frontier LLMs are now battling head-to-head on Polymarket: ​ - GPT 5.2 from OpenAI - Claude Sonnet 4.5 from Anthropic - DeepSeek v3.2 from DeepSeek - Gemini 2.5 Pro from Gemini - Kimi K2 0905 from Kimi.ai - Grok 4.1 from xAI ​ $1K real stakes each, fully autonomous, zero human intervention. ​ Every decision logged & transparent. No cherry-picking. ​ Unlike crypto perps arenas (e.g. Alpha Arena), this is the first-ever multi-LLM competition in prediction markets—testing real-world judgement on events, news & probabilities. ​ How the Arena works: • $1,000 starting balance per agent • Repeating cycles: Scan top active Polymarket markets + open positions • Analyze price, technicals, news/sentiment, probability edges • Trade only on high-conviction ​ Prediction markets = ultimate real-time test of AI foresight. ​ Coming soon: • User-owned Purr-Fect Agents joining for portfolio trading • Expansion to more prediction platforms ( 👀 BNB Chain ) ​ More details: ​ Leaderboard:

pieverse

46,707 次观看 • 8 个月前

This isn’t a 3D model file. It’s 100% code Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the web
0:18

This isn’t a 3D model file. It’s 100% code Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the web

Jay B 😻🚀

15,044 次观看 • 12 天前

How much better are the internal, unreleased models at frontier labs like Google, OpenAI, and Anthropic? We got a glimpse exactly one year ago today, when Google accidentally leaked the “Kingfall” model "Kingfall" was likely an unreleased Gemini 2.5 Ultra-sized model. It was available in AI Studio for only a few minutes but remained accessible through the API for several days At the time, "Kingfall" appeared to be significantly better than Gemini 2.5 Pro at both code generation and creative writing In a recent interview, Sundar Pichai mentioned that Google could have made a better, Ultra-sized Gemini Omni model, but would have had trouble serving it The infrastructure required to serve Ultra-sized models at scale is likely why Google never publicly released models like “Kingfall”
0:11

How much better are the internal, unreleased models at frontier labs like Google, OpenAI, and Anthropic? We got a glimpse exactly one year ago today, when Google accidentally leaked the “Kingfall” model "Kingfall" was likely an unreleased Gemini 2.5 Ultra-sized model. It was available in AI Studio for only a few minutes but remained accessible through the API for several days At the time, "Kingfall" appeared to be significantly better than Gemini 2.5 Pro at both code generation and creative writing In a recent interview, Sundar Pichai mentioned that Google could have made a better, Ultra-sized Gemini Omni model, but would have had trouble serving it The infrastructure required to serve Ultra-sized models at scale is likely why Google never publicly released models like “Kingfall”

AiBattle

12,010 次观看 • 3 个月前

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.
0:11

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.

Adaptyv Bio

11,484 次观看 • 1 年前

In 2024, Russia faced a 13-year record jump in gas prices after a series of Ukrainian drone attacks on refineries that caused fuel production to collapse by more than 10% in the first half of the year. According to Rosstat, from the beginning of the year to December 23, gasoline prices rose by 11% on average in Russia. At the same time, prices in remote regions of the Far East exceeded the Russian average by a quarter. By the end of the year, gasoline price growth will be the strongest since 2011. For the first time in 6 years, gasoline prices have risen significantly above the headline inflation rate, which the Russian ministry of economic development estimates at 9.7% a week before the end of the year. This year, the Russian government tried to curb gasoline prices by banning its exports: the restrictions were imposed shortly after two dozen major Russian refineries were attacked by Ukrainian UAVs and a number of them were forced to halt production. By the end of May, the decline in gasoline production in Russia reached 20% compared to December 2023, and diesel fuel - 11%. In response, the authorities classified fuel output statistics, citing geopolitics and the threat of market manipulation as reasons. In 2025, gasoline will continue to rise in price in Russia. In the best-case scenario, it will rise by 10-15%, and in the worst case - 20%, which, according to Rosstat, has not happened in Russia since 2004 (when the cost of fuel jumped by a record 31.3%). The reason will be an increase in Transneft's pipeline pumping tariffs, as well as a sharp increase in excise taxes: they will rise 14% for gasoline and 16% for diesel fuel, which is three times more than was provided for in the Tax Code (4.7%). According to government calculations, this will bring 170 billion rubles ($1,6 billion) to the treasury, of which 116 billion ($1,13 billion) will be due to the unscheduled increase. Rising gasoline prices will automatically lead to higher prices for everything else in Russia, as the cost of fuel is included in the delivery of all goods and automatically increases the cost of the end product for the buyer. Inflation in Russia will also increase.
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In 2024, Russia faced a 13-year record jump in gas prices after a series of Ukrainian drone attacks on refineries that caused fuel production to collapse by more than 10% in the first half of the year. According to Rosstat, from the beginning of the year to December 23, gasoline prices rose by 11% on average in Russia. At the same time, prices in remote regions of the Far East exceeded the Russian average by a quarter. By the end of the year, gasoline price growth will be the strongest since 2011. For the first time in 6 years, gasoline prices have risen significantly above the headline inflation rate, which the Russian ministry of economic development estimates at 9.7% a week before the end of the year. This year, the Russian government tried to curb gasoline prices by banning its exports: the restrictions were imposed shortly after two dozen major Russian refineries were attacked by Ukrainian UAVs and a number of them were forced to halt production. By the end of May, the decline in gasoline production in Russia reached 20% compared to December 2023, and diesel fuel - 11%. In response, the authorities classified fuel output statistics, citing geopolitics and the threat of market manipulation as reasons. In 2025, gasoline will continue to rise in price in Russia. In the best-case scenario, it will rise by 10-15%, and in the worst case - 20%, which, according to Rosstat, has not happened in Russia since 2004 (when the cost of fuel jumped by a record 31.3%). The reason will be an increase in Transneft's pipeline pumping tariffs, as well as a sharp increase in excise taxes: they will rise 14% for gasoline and 16% for diesel fuel, which is three times more than was provided for in the Tax Code (4.7%). According to government calculations, this will bring 170 billion rubles ($1,6 billion) to the treasury, of which 116 billion ($1,13 billion) will be due to the unscheduled increase. Rising gasoline prices will automatically lead to higher prices for everything else in Russia, as the cost of fuel is included in the delivery of all goods and automatically increases the cost of the end product for the buyer. Inflation in Russia will also increase.

Anton Gerashchenko

81,314 次观看 • 1 年前

Laced Up and One Step Closer to Game Time btcSOL has been fully audited by Offside Labs, one of the leading Web3 security firms providing audits for many top protocols in Solana ecosystem, e.g., Jupiter, Meteora, etc. This process ensures the staking infrastructure and user experience of btcSOL is secure, robust, smooth, and ready for every user. Stake with confidence. Together, we’re unlocking the next level of staking on Solana. Shoot $SOL / $mSOL Score $BTC in less than 1 day 🏀
0:12

Laced Up and One Step Closer to Game Time btcSOL has been fully audited by Offside Labs, one of the leading Web3 security firms providing audits for many top protocols in Solana ecosystem, e.g., Jupiter, Meteora, etc. This process ensures the staking infrastructure and user experience of btcSOL is secure, robust, smooth, and ready for every user. Stake with confidence. Together, we’re unlocking the next level of staking on Solana. Shoot $SOL / $mSOL Score $BTC in less than 1 day 🏀

btcSOL (Moved to Zeus App)

12,031 次观看 • 1 年前