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Kimi K2.5 (Kimi CLI) vs MiniMax 2.1 (CC) vs GLM 4.7 (CC). 🔥 Same prompt to create a single-page website for "PHANTOM PROTOCOL" a fictional tactical shooter video game, 0-shot. Spoiler IMO: 🥇 Kimi K2.5 is another league 🥈 MiniMax 2.1 🥉 GLM 4.7

Ivan Fioravanti ᯅ

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72,507 Aufrufe • vor 5 Monaten •via X (Twitter)

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Moritz Kremb

56,200 Aufrufe • vor 5 Monaten

NVIDIA is giving away free access to 130+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek-v4-flash, GPT-OSS-120B and 110+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "minimaxai/minimax-m2.7" model = "zhipuai/glm-5.1" model = "moonshot-ai/kimi-2.5" model = "deepseek-ai/deepseek-v4-flash" model = "nvidia/nemotron-3-ultra-550b-a55b" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free

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Mr. Buzzoni

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Shruti

185,672 Aufrufe • vor 4 Monaten

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Mr. Buzzoni

77,395 Aufrufe • vor 3 Tagen

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,319 Aufrufe • vor 1 Monat

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AI Frontliner

60,126 Aufrufe • vor 5 Monaten

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Pi Agent vs OpenCode token usage A lot of people recommended Pi Agent so I decided to check Pi Agent took 1.1k tokens in first turn OpenCode took 11.5k Setup: 1) Trimmed OpenCode (from usual 30k first turn to 11.5k) - 0 MCPs - 2 lightweight plugins (opencode-env-protect and openslimedit) - 8k char AGENTS.md - 11585 deepseek-v4-flash input tokens - for $0.0016 2) Vanilla Pi - 0 MCPs - 0 system prompt - 8k char AGENTS.md - 1114 kimi-k2.6 input tokens - for $0.0008 I think using better models with capped tokens per turn can keep usage nearly the same as uncontrolled DeepSeek V4 Flash? The challenge now is finding the sweet spot. Can't cap tokens if quality drops. We'll see. Video below - OpenCode vs Pi side by side "say hi back" first turns test, with OpenCode Go usage for each

raymel 👋

201,405 Aufrufe • vor 2 Monaten

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GLM-5.1 > Claude Code (Opus 4.6)? I'm tripping or CC has become very bad but built a Three.js racing game to eval and it's extremely impressive. Thoughts: - One-shot car physics with real drift mechanics (this is hard) - My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen - 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters - All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio! - Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed You are going to hear about this model a lot in the next months - open source let's go 🚀🚀

Victor M

193,488 Aufrufe • vor 3 Monaten

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 Kimi.ai just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, Three.js, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on AI/ML API platform results: - cost #1 grok 4.5 – $0.30 #2 kimi k3 – $0.71 #3 gpt 5.6 sol – $2.05 #4 fable 5 – $7.69 - tokens #1 grok 4.5 – 34,241 #2 gpt 5.6 sol – 51,748 #3 fable 5 – 144,126 #4 kimi k3 – 157,999 - lines of code #1 gpt 5.6 sol – 3,054 #2 grok 4.5 – 3,047 #3 kimi k3 – 2,255 #4 fable 5 – 1,950 - generation time #1 grok 4.5 – 5.1 min #2 gpt 5.6 sol – 22.0 min #3 fable 5 – 31.5 min #4 kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow thehype. for 24/7 ai news, analysis and breakdowns

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 Kimi.ai just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, Three.js, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on AI/ML API platform results: - cost #1 grok 4.5 – $0.30 #2 kimi k3 – $0.71 #3 gpt 5.6 sol – $2.05 #4 fable 5 – $7.69 - tokens #1 grok 4.5 – 34,241 #2 gpt 5.6 sol – 51,748 #3 fable 5 – 144,126 #4 kimi k3 – 157,999 - lines of code #1 gpt 5.6 sol – 3,054 #2 grok 4.5 – 3,047 #3 kimi k3 – 2,255 #4 fable 5 – 1,950 - generation time #1 grok 4.5 – 5.1 min #2 gpt 5.6 sol – 22.0 min #3 fable 5 – 31.5 min #4 kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

1,395,120 Aufrufe • vor 17 Stunden

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

2xnmore

27,019 Aufrufe • vor 1 Monat

Researchers found a way to make LLMs 8.5x faster! (without compromising accuracy) Speculative decoding is quite an effective way to address the single-token bottleneck in traditional LLM inference. A small "draft" model first generates the next several tokens, then the large model verifies all of them at once in a single forward pass. If a token at any position is wrong, you keep everything before it and restart from there. This never does worse than normal decoding. But current drafters in Speculative decoding still guess one token at a time. That makes the drafting step itself a bottleneck, capping real-world speedups at 2-3x. DFlash is a new technique that swaps the autoregressive drafter with a lightweight block diffusion model that guesses all tokens in one parallel shot. Drafting cost stays flat no matter how many tokens you speculate. On top of that, the drafter is conditioned on hidden features pulled from multiple layers of the target model and injected into every draft layer, so it makes significantly better guesses than a drafter working from scratch. In the side-by-side demo below, vanilla decoding runs at 48.5 tokens/sec. DFlash hits 415 tokens/sec on the same model, with zero quality loss. It's already integrated with vLLM, SGLang, and Transformers, with draft models on HuggingFace for several models like Qwen3, Qwen3.5, Llama 3.1, Kimi-K2.5, gpt-oss, and many more. I have shared the GitHub repo in the replies! KV caching is another must-know technique to boost LLM inference. I recently wrote an article about it. Read it below. 👉 Over to you: What use case are you working on that can benefit from this new technique?

Researchers found a way to make LLMs 8.5x faster! (without compromising accuracy) Speculative decoding is quite an effective way to address the single-token bottleneck in traditional LLM inference. A small "draft" model first generates the next several tokens, then the large model verifies all of them at once in a single forward pass. If a token at any position is wrong, you keep everything before it and restart from there. This never does worse than normal decoding. But current drafters in Speculative decoding still guess one token at a time. That makes the drafting step itself a bottleneck, capping real-world speedups at 2-3x. DFlash is a new technique that swaps the autoregressive drafter with a lightweight block diffusion model that guesses all tokens in one parallel shot. Drafting cost stays flat no matter how many tokens you speculate. On top of that, the drafter is conditioned on hidden features pulled from multiple layers of the target model and injected into every draft layer, so it makes significantly better guesses than a drafter working from scratch. In the side-by-side demo below, vanilla decoding runs at 48.5 tokens/sec. DFlash hits 415 tokens/sec on the same model, with zero quality loss. It's already integrated with vLLM, SGLang, and Transformers, with draft models on HuggingFace for several models like Qwen3, Qwen3.5, Llama 3.1, Kimi-K2.5, gpt-oss, and many more. I have shared the GitHub repo in the replies! KV caching is another must-know technique to boost LLM inference. I recently wrote an article about it. Read it below. 👉 Over to you: What use case are you working on that can benefit from this new technique?

Avi Chawla

157,390 Aufrufe • vor 2 Monaten

$I just compared Claude Code vs Codex vs Cursor CLI The task was to build a Next.js app with Tailwind 4 and shadcn components to collect customer feedback and showcase it with a widget. I gave all three the same prompt and let them go for 30 minutes to see what they came up with. Claude Code with Opus 4.1 Even though I told it to set up the app in the existing project folder, it tried to create a directory for it. After I interrupted and told it not to do that, it built a demo form and landing page with no errors. I had to ask it to make the demo interactive so users could submit a testimonial and preview it. The landing page looked like AI and was pretty basic, but it worked and it was done in a fraction of the time of the others. Total tokens used: 33k Codex with GPT-5 At the end of the 30 minutes I just could not get Codex to produce a working app. It got stuck in a loop of not being able to set up Tailwind 4 and despite many, MANY, attempts, I ended up with a "failed to compile" error. Total tokens used: 102k Cursor Agent with GPT-5 This was the slowest agent by far and a couple of times I actually thought it got stuck in a loop and was close to Ctrl+C'ing to cancel it. The TUI is really nice though, especially how it shows diffs and it did eventually build a working app (after one or two slight errors that needed fixing) The demo was interactive and it had a very minimal design that looked bare but also a lot less like an "AI generated" app than the Opus 4.1 design. It also wasn't too chatty and just did what it needed to do! Code quality was on a par with Opus 4.1, but it did use 5.5x as many tokens to get there. Still cheaper than Opus on a direct comparison but not when you factor in a Claude Code Max subscription. Total tokens: 188k I'll be able to do a proper comparison and record some videos when I'm back from holiday but for now, Opus is still the more capable model out of the box and Claude Code is the more complete CLI product. It will be interesting to see how Cursor evolve their CLI though with commands and subagents because I think with GPT-5 they have a real shot at providing competition for Claude Code if they can optimise output to get similar quality with less tokens. Jump to 0:40 in the video to see the two apps. Which do you think is which? ;)$

I just compared Claude Code vs Codex vs Cursor CLI The task was to build a Next.js app with Tailwind 4 and shadcn components to collect customer feedback and showcase it with a widget. I gave all three the same prompt and let them go for 30 minutes to see what they came up with. Claude Code with Opus 4.1 Even though I told it to set up the app in the existing project folder, it tried to create a directory for it. After I interrupted and told it not to do that, it built a demo form and landing page with no errors. I had to ask it to make the demo interactive so users could submit a testimonial and preview it. The landing page looked like AI and was pretty basic, but it worked and it was done in a fraction of the time of the others. Total tokens used: 33k Codex with GPT-5 At the end of the 30 minutes I just could not get Codex to produce a working app. It got stuck in a loop of not being able to set up Tailwind 4 and despite many, MANY, attempts, I ended up with a "failed to compile" error. Total tokens used: 102k Cursor Agent with GPT-5 This was the slowest agent by far and a couple of times I actually thought it got stuck in a loop and was close to Ctrl+C'ing to cancel it. The TUI is really nice though, especially how it shows diffs and it did eventually build a working app (after one or two slight errors that needed fixing) The demo was interactive and it had a very minimal design that looked bare but also a lot less like an "AI generated" app than the Opus 4.1 design. It also wasn't too chatty and just did what it needed to do! Code quality was on a par with Opus 4.1, but it did use 5.5x as many tokens to get there. Still cheaper than Opus on a direct comparison but not when you factor in a Claude Code Max subscription. Total tokens: 188k I'll be able to do a proper comparison and record some videos when I'm back from holiday but for now, Opus is still the more capable model out of the box and Claude Code is the more complete CLI product. It will be interesting to see how Cursor evolve their CLI though with commands and subagents because I think with GPT-5 they have a real shot at providing competition for Claude Code if they can optimise output to get similar quality with less tokens. Jump to 0:40 in the video to see the two apps. Which do you think is which? ;)

Ian Nuttall

194,949 Aufrufe • vor 11 Monaten

( Athletes who have Kobe ranked over LeBron ‼️ )👇👇 James Harden, Kevin Looney, Kevin Durant, Paul George, Shannon Brown, Shai Gilgeous-Alexander, Mike Bibby, Kyrie Irving, Devin Booker, Stehpon Marbury, Jerry West, Shaq, Cody Williams, Allen Iverson, Charles Barkley, Kenny Anderson, Brandon Jennings, Steph Curry, Matas Buzelis, Dirk, Rob Dillingham, Jamal Crawford, Bonzi Wells, Mark Jackson, DeMar Derozen, Horace Grant, Stephon Marbury, Tyson Chandler, Anthony Edwards, Tony Parker, Bruce Bowen, Jim Jackson, Kenyon Martin, Manu Ginóbli, Kevin Garnett, Kawhi Leonard, Tracy McGrady, Vince Carter, QRich, Lamar Odom, Greg Anthony, Julius Randle, Darius Myles, Jeremy Lin, Matt Barnes, Jordan Clarkson, Isaiah Thomas (not the 90s IT), Avery Johnson, Glen Rice, Colin Sexton, Caron Butler, Chris Dunn, Joe Smith, Tervor Ariza, Robert Horry, Nate Robinson, Stephen Jackson, Grant Hill, Tony Allen, & Marreese Speights & Steve Nash. I Will Never Put Lebron Above Kobe!!!!! Kobe is a Better :Shooter ,Scorer ,Post game better, Kobes Footwork is Better ,Kobe has better handles ,more skilled , Moves/ counter moves better ,Better off the ball ,Peak scoring higher ,Defender ,Winner ( 5-2) > (3-6) 🤫Kobe Also Played Against Tougher Competition 🤫 And Kobe is a better Leader Kobe Never made excuses for his failures and he didn’t have switch teams & Join superstars To Win Championships 🫡💯 🐍 Kobe Bryant: 8-2 5-2 Finals 🥇2000 NBA finals 🥇2001 nba finals 🥇2002 NBA Finals 🥈2004 NBA Finals 🥈2008 nba Finals 🥇07 FIBA America’s Cup 🥇08 Olympics 🥇09 NBA Finals 🥇10 NBA finals 🥇’12 Olympics Mamba 👑 LeBron James: 8-8 4-6 Finals 🥉’04 Olympics 🥉 ‘06 FIBA World Cup 🥈2007 nba finals 🥇’07 FIBA America’s Cup 🥇 ‘08 Olympics 🥈2011 NBA Finals 🥇’12 Olympics 🥈2014 NBA Finals 🥈2015 NBA Finals 🥈2017 NBA Finals 🥈2018 NBA finals 🥇 ‘24 Olympics Lebron Also Had More Help Than Kobe & Jordan Combined & Also played against worse competition And Still has a losing record in the nba finals Kobe Bryant Vs LeBron James ~ Age 21-34 ~ Both Played 1039 Games Kobe LeBron 27.8✅ PPG 27.6❌ 28,862✅ Total PTS 28,714 ❌ 96✅ 40+ Games 47 ❌ 24✅ 50+ Games 10❌ 5✅ 60+ Games 1 ❌ 1✅ 80+ Games 0❌ 2✅ Scoring Titles 1❌ 64%❌ 0-3FT 74%✅ 45%✅ 3-10FT 43%❌ 44%✅ 10-16FT 38%❌ 41%✅ 16-3PT 39% ❌ 34%✅ 3PT 35% ✅ 85%✅ FT 73% ❌ 5✅ Championships 3 ❌ 1❌ MVPs 4 ✅ 12✅ All-DEF 6 ❌ 2❌ FMVP 4 ✅ Finals Production 28.8✅ PPG 27.5 ❌ 3.0✅ TOV 3.5 ❌ 1.5❌ STL 1.8✅ 82.5%✅ FT 72.4%✅ 0.9🟰 BLK 0.9🟰 35.6%✅ 3PT 33.6❌ 63.6%✅ C 3PT 38.4%❌ 91.6✅ Pace 94.8 ❌ 5.4❌ AST 7.1 ✅ 6.1❌ REB 9.6 ✅ 45.4%❌ FG 51.9 ✅ LeBron : 4 Rings❌ 0 3-Peats❌ 40% Finals Win Rate ❌ 83% Conference Finals ❌ 🥇Win Rate 60% ❌ 64% Playoff Win Rate✅ 43% Playoff Win Rate Vs Top 75 Players❌ Game 7 Win Rate 75% ❌ Kobe : 5 Rings ✅ 1 3-Peat ✅ 70% Finals Win Rate ✅ 87% Conference Finals Win Rate ✅ 🥇Win Rate 100%✅ 61% Playoff Win Rate ❌ 61% Playoff Win Rate Vs Top 75 Players✅ Game 7 Win Rate 83% ✅ Tougher Competition: Kobe ✅ Less Help : Kobe ✅

( Athletes who have Kobe ranked over LeBron ‼️ )👇👇 James Harden, Kevin Looney, Kevin Durant, Paul George, Shannon Brown, Shai Gilgeous-Alexander, Mike Bibby, Kyrie Irving, Devin Booker, Stehpon Marbury, Jerry West, Shaq, Cody Williams, Allen Iverson, Charles Barkley, Kenny Anderson, Brandon Jennings, Steph Curry, Matas Buzelis, Dirk, Rob Dillingham, Jamal Crawford, Bonzi Wells, Mark Jackson, DeMar Derozen, Horace Grant, Stephon Marbury, Tyson Chandler, Anthony Edwards, Tony Parker, Bruce Bowen, Jim Jackson, Kenyon Martin, Manu Ginóbli, Kevin Garnett, Kawhi Leonard, Tracy McGrady, Vince Carter, QRich, Lamar Odom, Greg Anthony, Julius Randle, Darius Myles, Jeremy Lin, Matt Barnes, Jordan Clarkson, Isaiah Thomas (not the 90s IT), Avery Johnson, Glen Rice, Colin Sexton, Caron Butler, Chris Dunn, Joe Smith, Tervor Ariza, Robert Horry, Nate Robinson, Stephen Jackson, Grant Hill, Tony Allen, & Marreese Speights & Steve Nash. I Will Never Put Lebron Above Kobe!!!!! Kobe is a Better :Shooter ,Scorer ,Post game better, Kobes Footwork is Better ,Kobe has better handles ,more skilled , Moves/ counter moves better ,Better off the ball ,Peak scoring higher ,Defender ,Winner ( 5-2) > (3-6) 🤫Kobe Also Played Against Tougher Competition 🤫 And Kobe is a better Leader Kobe Never made excuses for his failures and he didn’t have switch teams & Join superstars To Win Championships 🫡💯 🐍 Kobe Bryant: 8-2 5-2 Finals 🥇2000 NBA finals 🥇2001 nba finals 🥇2002 NBA Finals 🥈2004 NBA Finals 🥈2008 nba Finals 🥇07 FIBA America’s Cup 🥇08 Olympics 🥇09 NBA Finals 🥇10 NBA finals 🥇’12 Olympics Mamba 👑 LeBron James: 8-8 4-6 Finals 🥉’04 Olympics 🥉 ‘06 FIBA World Cup 🥈2007 nba finals 🥇’07 FIBA America’s Cup 🥇 ‘08 Olympics 🥈2011 NBA Finals 🥇’12 Olympics 🥈2014 NBA Finals 🥈2015 NBA Finals 🥈2017 NBA Finals 🥈2018 NBA finals 🥇 ‘24 Olympics Lebron Also Had More Help Than Kobe & Jordan Combined & Also played against worse competition And Still has a losing record in the nba finals Kobe Bryant Vs LeBron James ~ Age 21-34 ~ Both Played 1039 Games Kobe LeBron 27.8✅ PPG 27.6❌ 28,862✅ Total PTS 28,714 ❌ 96✅ 40+ Games 47 ❌ 24✅ 50+ Games 10❌ 5✅ 60+ Games 1 ❌ 1✅ 80+ Games 0❌ 2✅ Scoring Titles 1❌ 64%❌ 0-3FT 74%✅ 45%✅ 3-10FT 43%❌ 44%✅ 10-16FT 38%❌ 41%✅ 16-3PT 39% ❌ 34%✅ 3PT 35% ✅ 85%✅ FT 73% ❌ 5✅ Championships 3 ❌ 1❌ MVPs 4 ✅ 12✅ All-DEF 6 ❌ 2❌ FMVP 4 ✅ Finals Production 28.8✅ PPG 27.5 ❌ 3.0✅ TOV 3.5 ❌ 1.5❌ STL 1.8✅ 82.5%✅ FT 72.4%✅ 0.9🟰 BLK 0.9🟰 35.6%✅ 3PT 33.6❌ 63.6%✅ C 3PT 38.4%❌ 91.6✅ Pace 94.8 ❌ 5.4❌ AST 7.1 ✅ 6.1❌ REB 9.6 ✅ 45.4%❌ FG 51.9 ✅ LeBron : 4 Rings❌ 0 3-Peats❌ 40% Finals Win Rate ❌ 83% Conference Finals ❌ 🥇Win Rate 60% ❌ 64% Playoff Win Rate✅ 43% Playoff Win Rate Vs Top 75 Players❌ Game 7 Win Rate 75% ❌ Kobe : 5 Rings ✅ 1 3-Peat ✅ 70% Finals Win Rate ✅ 87% Conference Finals Win Rate ✅ 🥇Win Rate 100%✅ 61% Playoff Win Rate ❌ 61% Playoff Win Rate Vs Top 75 Players✅ Game 7 Win Rate 83% ✅ Tougher Competition: Kobe ✅ Less Help : Kobe ✅

8/24MαɱႦα.Aɾƈԋιʋҽʂ🐐

222,267 Aufrufe • vor 1 Jahr

My AI made Shopify pages are getting better and better everyday Here’s an example of a Shopify section I built with the reference page and the result on my store 👇 Guide: To make good AI landing pages, you need to use the same method as making good AI UGC or AI product images you have to take something that's already good as a reference for Claude/gemini to analyze and adapt for your product/brand for now you can't adapt full landing pages to your product because the Claude context gets bloated fast and you get poor output. Listicles are the only kind of page you can "one shot" with Claude. But Product pages are another story Analyze an existing page and adapting it to your product section by section is the way to go. The results are 10x better. here's an example (on the video): 1- I found this good product section from im8's product page. I screen-recorded the section on both desktop and mobile, going through the animations to capture the dynamism that a screenshot wouldn’t show. Then I asked Claude Web or Gemini to analyze the recording and produce a very detailed report. Full prompt is on my TG channel, it's quite long. 2- then ask Claude (inside your Shopify brand project folder): "I have a detailed UI/UX specification document for a product page section I want to adapt to my product. Recreate this exactly on Shopify as a section template and adapt it to [name of the product] using the brand guidelines" (paste the result prompt from step 1 ). 3- you will have your section ready after 3-4 minutes. you'll probably have to change a few things. spacing, small visual bugs, price not appearing correctly. it will take you 5 minutes maximum. 4- then you can ask Claude to make 4 different variations of the section using different designs and pick the best one using this prompt: "Create 4 design variations of this section. Keep the content and layout structure identical across all, only vary the visual treatment (color usage, typography hierarchy, spacing, component styling). I will choose the one I like the most." after that you have a pretty good section, and you can do the process again for all sections of the page. The less complex the section, the faster the process will be. note: I know the AI result is not perfect, but it's pretty impressive imo and it will only get better.

Olivier

84,679 Aufrufe • vor 4 Monaten