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Fable 5 vs GPT-5.6 SOL: Epic Samurai Battle on arcads AI 🔥 Watch a fierce samurai emerge from a blazing multi-tiered Japanese castle at night, with explosive flames, dynamic sword action, swirling fire rings, and powerful close-ups of glowing-eyed helmets. Left: Anthropic's Fable 5 Right: OpenAI's GPT-5.6 SOL

Sharon Riley

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Most benchmarks just give you a number on a chart. We made one where you judge the actual work side-by-side. Here's Fable 5 vs GPT-5.6 Sol on a mix of design, writing, and creative work. Try Hyperbench and choose your winner:

Most benchmarks just give you a number on a chart. We made one where you judge the actual work side-by-side. Here's Fable 5 vs GPT-5.6 Sol on a mix of design, writing, and creative work. Try Hyperbench and choose your winner:

Hyperagent

125,171 görüntüleme • 2 ay önce

You can now orchestrate Fable 5, Sol, and any model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.

You can now orchestrate Fable 5, Sol, and any model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.

Alvaro Cintas

92,149 görüntüleme • 2 ay önce

$GLM-5.3 beat GPT-5.6 SOL at 3D Z.ai 's GLM-5.3 vs OpenAI's GPT-5.6 SOL, both through one API (aimlapi[.]com) the test: one self-contained three.js file rendering Godzilla destroying a night megacity — 150+ lit skyscrapers, real physics on collapsing buildings, then eye lasers carving the skyline. 18-second loop cost: GLM-5.3 — $0.37 GPT-5.6 SOL — $0.44 what we saw: • GLM's destruction is the real deal — towers fracture into chunks that tumble, pile up and burn. • GLM's Godzilla actually looks like a Godzilla — dark scales, weighty walk, menacing silhouette. SOL shipped a smooth pale toy • GLM packed the frame — dense city, glowing windows, moon, fires everywhere. SOL's city is sparser and flatter$

GLM-5.3 beat GPT-5.6 SOL at 3D Z.ai 's GLM-5.3 vs OpenAI's GPT-5.6 SOL, both through one API (aimlapi[.]com) the test: one self-contained three.js file rendering Godzilla destroying a night megacity — 150+ lit skyscrapers, real physics on collapsing buildings, then eye lasers carving the skyline. 18-second loop cost: GLM-5.3 — $0.37 GPT-5.6 SOL — $0.44 what we saw: • GLM's destruction is the real deal — towers fracture into chunks that tumble, pile up and burn. • GLM's Godzilla actually looks like a Godzilla — dark scales, weighty walk, menacing silhouette. SOL shipped a smooth pale toy • GLM packed the frame — dense city, glowing windows, moon, fires everywhere. SOL's city is sparser and flatter

AI/ML API

14,357 görüntüleme • 1 ay önce

we've eval-ing K3 and we were legit shocked K3 is incredibly good at HyperFrames , winning over GPT-5.6 and Fable 5 at a few of our benchmarks it recreated motion graphics from leo with minimal prompting (i didn't further tune the animations to show raw output)

we've eval-ing K3 and we were legit shocked K3 is incredibly good at HyperFrames , winning over GPT-5.6 and Fable 5 at a few of our benchmarks it recreated motion graphics from leo with minimal prompting (i didn't further tune the animations to show raw output)

Bin Liu

229,677 görüntüleme • 2 ay önce

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.

atomic.chat

70,490 görüntüleme • 2 ay önce

I tested Qwen 3.8 Max vs Kimi K3 Same scene, lighting, props, detail requirements. Qwen's output is decent, but it's not close to Kimi K3. Qwen's output came out messy, text handling was off, and the composition felt unorganized next to Kimi's version. In my opinion, Qwen isn't second to Fable 5 either, and it's also below GPT-5.6 Sol. It's a solid model, but not at that tier. Kimi K3 is still clearly ahead here.

I tested Qwen 3.8 Max vs Kimi K3 Same scene, lighting, props, detail requirements. Qwen's output is decent, but it's not close to Kimi K3. Qwen's output came out messy, text handling was off, and the composition felt unorganized next to Kimi's version. In my opinion, Qwen isn't second to Fable 5 either, and it's also below GPT-5.6 Sol. It's a solid model, but not at that tier. Kimi K3 is still clearly ahead here.

Bhavy☄️

34,045 görüntüleme • 2 ay önce

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

Bhavy☄️

479,495 görüntüleme • 2 ay önce

The gap between “here’s the design” and “here’s the working app” is getting very small. Grok 4.6 took #1 on the VISTA leaderboard, beating Claude Fable 5, Opus 5, and GPT-5.6 Sol. It has to look at a real Figma design, figure out the structure, recreate the visuals, and build the functioning app from it. And it finished the benchmark at roughly $2.38 per task. That’s the kind of shortcut builders actually care about. Grok SpaceXAI / Writer: Annette, Grok Imagine Designer: Janné

The gap between “here’s the design” and “here’s the working app” is getting very small. Grok 4.6 took #1 on the VISTA leaderboard, beating Claude Fable 5, Opus 5, and GPT-5.6 Sol. It has to look at a real Figma design, figure out the structure, recreate the visuals, and build the functioning app from it. And it finished the benchmark at roughly $2.38 per task. That’s the kind of shortcut builders actually care about. Grok SpaceXAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

60,527 görüntüleme • 1 ay önce

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,387 görüntüleme • 2 ay önce

GPT-5.6 Sol is unbelievably good at creating and editing videos. It can do motion design, product demos, and animations like this one I made by simply giving it a screen recording. GPT 5.6 has the best design taste and significantly outperforms Fable, which relies heavily on repetitive design patterns. To help you experiment with video editing on it, we just launched a collection of 100 ready-to-use skills that show what’s possible and help you get started with video editing using GPT-5.6. These skills can create anything from motion graphics launch videos for your product to a 3B1B-style science explainer video. You can also use them to edit existing videos: add captions, generate motion graphics, create voiceovers, redesign visual styles, translate into new languages, and much more. If you want access to the full library, comment “VIDEO SKILLS” and I’ll share it with you. (You'll have to follow me so I can DM you.)

GPT-5.6 Sol is unbelievably good at creating and editing videos. It can do motion design, product demos, and animations like this one I made by simply giving it a screen recording. GPT 5.6 has the best design taste and significantly outperforms Fable, which relies heavily on repetitive design patterns. To help you experiment with video editing on it, we just launched a collection of 100 ready-to-use skills that show what’s possible and help you get started with video editing using GPT-5.6. These skills can create anything from motion graphics launch videos for your product to a 3B1B-style science explainer video. You can also use them to edit existing videos: add captions, generate motion graphics, create voiceovers, redesign visual styles, translate into new languages, and much more. If you want access to the full library, comment “VIDEO SKILLS” and I’ll share it with you. (You'll have to follow me so I can DM you.)

Akash Anand

517,594 görüntüleme • 2 ay önce

this is f**king insane i cancelled my $20/mo claude subscription for this. a dev found a way around OpenAI's usage limits and got unlimited GPT-5.6 Luna on Codex for FREE ($0)😳 He built a three step local workflow on a single machine. This involved no servers, shared accounts, or a team. Instead he used a GitHub repo, one prompt file, and a local coding agent. Traditional users pay full price or hit usage limits. His cost: basically $0. Here's the set up (3min): → Repo layer: Installed 'sol-advisor' from GitHub. → Prompt layer: Copied the 'savings-prompt' out of the repo. → Agent layer: Pointed codex at gpt 5.6 luna with the prompt loaded. → Activation: Loaded it into codex. Usage limits gone. usage after activation: normal codex access. unlimited. one trick. one window. use this before it gets patched.

this is f**king insane i cancelled my $20/mo claude subscription for this. a dev found a way around OpenAI's usage limits and got unlimited GPT-5.6 Luna on Codex for FREE ($0)😳 He built a three step local workflow on a single machine. This involved no servers, shared accounts, or a team. Instead he used a GitHub repo, one prompt file, and a local coding agent. Traditional users pay full price or hit usage limits. His cost: basically $0. Here's the set up (3min): → Repo layer: Installed 'sol-advisor' from GitHub. → Prompt layer: Copied the 'savings-prompt' out of the repo. → Agent layer: Pointed codex at gpt 5.6 luna with the prompt loaded. → Activation: Loaded it into codex. Usage limits gone. usage after activation: normal codex access. unlimited. one trick. one window. use this before it gets patched.

painn

98,056 görüntüleme • 1 ay önce

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

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

32,974 görüntüleme • 1 ay önce

When darkness rises, legends answer. One flaming strike is all it takes to change destiny Credit: SeaArt.Ai🐋 SeaArt Creator Lab Prompt: Epic fantasy battle scene in a volcanic wasteland at night. A towering demonic monster with glowing lava cracks across its body faces a fearless armored warrior. The camera rapidly orbits around the combatants in a fast cinematic 360-degree motion, creating intense speed and energy. Sparks, ash, and embers swirl through the air as the ground trembles beneath them. The demon suddenly lunges forward and swings one of its enormous razor-sharp claws in a devastating attack. The claw tears through the air with explosive force, generating shockwaves, flying debris, and motion blur. At the exact moment of impact, the warrior dashes forward fearlessly. The warrior's legendary sword ignites with blazing orange and golden flames, fire wrapping around the blade and leaving a bright trail of sparks. In a dramatic slow-motion moment, the warrior unleashes a powerful flaming slash directly toward the demon. The fiery sword arc cuts through the darkness, illuminating the battlefield with intense light. Flames erupt outward as the attack connects, creating a massive explosion of fire, sparks, smoke, and molten energy. Ultra-detailed cinematic visuals, dynamic camera movement, dramatic lighting, volumetric smoke, realistic fire simulation, epic fantasy atmosphere, high contrast, action-packed choreography, motion blur, particle effects, AAA game cinematic quality, Unreal Engine 5 style, 4K, highly detailed textures, masterpiece, intense and heroic mood.

When darkness rises, legends answer. One flaming strike is all it takes to change destiny Credit: SeaArt.Ai🐋 SeaArt Creator Lab Prompt: Epic fantasy battle scene in a volcanic wasteland at night. A towering demonic monster with glowing lava cracks across its body faces a fearless armored warrior. The camera rapidly orbits around the combatants in a fast cinematic 360-degree motion, creating intense speed and energy. Sparks, ash, and embers swirl through the air as the ground trembles beneath them. The demon suddenly lunges forward and swings one of its enormous razor-sharp claws in a devastating attack. The claw tears through the air with explosive force, generating shockwaves, flying debris, and motion blur. At the exact moment of impact, the warrior dashes forward fearlessly. The warrior's legendary sword ignites with blazing orange and golden flames, fire wrapping around the blade and leaving a bright trail of sparks. In a dramatic slow-motion moment, the warrior unleashes a powerful flaming slash directly toward the demon. The fiery sword arc cuts through the darkness, illuminating the battlefield with intense light. Flames erupt outward as the attack connects, creating a massive explosion of fire, sparks, smoke, and molten energy. Ultra-detailed cinematic visuals, dynamic camera movement, dramatic lighting, volumetric smoke, realistic fire simulation, epic fantasy atmosphere, high contrast, action-packed choreography, motion blur, particle effects, AAA game cinematic quality, Unreal Engine 5 style, 4K, highly detailed textures, masterpiece, intense and heroic mood.

Hope Ai

17,560 görüntüleme • 2 ay önce

From the frozen depths to the heart of the flame Watch the ultimate transformation. Made with Seedance 2.0 Prompt: A continuous cinematic fantasy action sequence of a young East Asian warrior woman. She is crawling on a cracked icy battlefield, desperately holding up a glowing teardrop-shaped red crystal amulet set in a gold crescent. Suddenly, a massive surge of fiery energy explodes from the crystal, instantly shattering the ice and turning the entire landscape into a volcanic crater filled with flowing magma and ruined stone pillars. The woman levitates high into the air as intense, swirling vortices of fire wrap around her body. In mid-air, the flames materialize into a highly detailed crimson and gold fantasy dress with metallic arm guards and a flowing cape. A blazing crown of pure fire forms on her forehead, and her eyes snap open, glowing with intense, fiery heart-shaped patterns. She raises her hand to summon an intricate, giant golden magical circle in front of her, unleashing a catastrophic, blinding explosion of fire that obliterates everything in sight. 8K ultra-realism, high-fidelity VFX, dynamic camera angles, cinematic lighting, and epic scale.

From the frozen depths to the heart of the flame Watch the ultimate transformation. Made with Seedance 2.0 Prompt: A continuous cinematic fantasy action sequence of a young East Asian warrior woman. She is crawling on a cracked icy battlefield, desperately holding up a glowing teardrop-shaped red crystal amulet set in a gold crescent. Suddenly, a massive surge of fiery energy explodes from the crystal, instantly shattering the ice and turning the entire landscape into a volcanic crater filled with flowing magma and ruined stone pillars. The woman levitates high into the air as intense, swirling vortices of fire wrap around her body. In mid-air, the flames materialize into a highly detailed crimson and gold fantasy dress with metallic arm guards and a flowing cape. A blazing crown of pure fire forms on her forehead, and her eyes snap open, glowing with intense, fiery heart-shaped patterns. She raises her hand to summon an intricate, giant golden magical circle in front of her, unleashing a catastrophic, blinding explosion of fire that obliterates everything in sight. 8K ultra-realism, high-fidelity VFX, dynamic camera angles, cinematic lighting, and epic scale.

Zyrella

26,053 görüntüleme • 2 ay önce

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5

YanXbt

16,744 görüntüleme • 2 ay önce

Claude Fable 5 + Claude Design is f*cking insane 🤯 Anthropic just dropped its most intelligent model ever, and the first thing I pointed it at was email design. I built a complete email campaign design in Claude Design, and the difference is night and day: tighter layouts, cleaner hierarchy, on-brand from the first generation. All inside Claude Design with Fable 5. Perfect for DTC brands and agencies who are still paying email agencies $3-5K/month for campaign designs that take 2 weeks to ship. If your campaign calendar is packed but every new email means briefing a designer, waiting on mockups, sending notes, and waiting again... This workflow eliminates the entire bottleneck: → Load your brand design system into Claude Design once (colors, fonts, logo, button styling) → Switch the model to Claude Fable 5 — Anthropic's new state-of-the-art model with the best vision of any AI → Prompt the campaign email section by section: header, hero, headline, offer block, CTA → Fable 5 nails layout and brand details that older models fumbled → Iterate inline — swap images, adjust styling, color-pick directly in the canvas → Export the finished email and hand off to your ESP No briefing a designer. No 2-week turnaround on a single campaign. No paying an agency $4K/month for 4 emails. What you get: → Campaign emails designed in minutes, not weeks → A reusable design system every new email pulls from automatically → Noticeably smarter design decisions from Fable 5's upgraded vision → Full inline editing before anything touches your ESP Built 100% with Claude Design + Claude Fable 5. I recorded a full walkthrough showing exactly how this works. Want it for free? > Like this post > Comment "FABLE" And I'll send it over (must be following so I can DM)

Mike Futia

43,201 görüntüleme • 3 ay önce

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

179,451 görüntüleme • 2 ay önce

From fear to unstoppable power 🌙 A cinematic AI transformation brought to life with epic storytelling stunning visuals and blockbuster energy Created with seedance 2.0 Prompt: A cinematic, high-budget live-action sequence. An injured young East Asian woman is crawling on a city plaza floor as terrifying, faceless dark-skinned monsters close in on her. She raises a glowing, metallic sci-fi crescent amulet and screams "Moonlight awaken!". A sudden flash of white light transitions to night, where she is suspended in the air beneath a massive, glowing full moon. Energy surges up her body, transforming her casual clothes into a sleek white and silver sci-fi battle uniform with a flowing cape and a glowing crescent tiara. Her eyes open, glowing entirely bright white. She lands dynamically, wielding a glowing crescent staff, and releases a powerful energy wave that disintegrates all surrounding monsters into dust. Ultra-realistic, hyper-detailed, epic sci-fi fantasy, dramatic lighting, 8k resolution.

From fear to unstoppable power 🌙 A cinematic AI transformation brought to life with epic storytelling stunning visuals and blockbuster energy Created with seedance 2.0 Prompt: A cinematic, high-budget live-action sequence. An injured young East Asian woman is crawling on a city plaza floor as terrifying, faceless dark-skinned monsters close in on her. She raises a glowing, metallic sci-fi crescent amulet and screams "Moonlight awaken!". A sudden flash of white light transitions to night, where she is suspended in the air beneath a massive, glowing full moon. Energy surges up her body, transforming her casual clothes into a sleek white and silver sci-fi battle uniform with a flowing cape and a glowing crescent tiara. Her eyes open, glowing entirely bright white. She lands dynamically, wielding a glowing crescent staff, and releases a powerful energy wave that disintegrates all surrounding monsters into dust. Ultra-realistic, hyper-detailed, epic sci-fi fantasy, dramatic lighting, 8k resolution.

Zyrella

29,540 görüntüleme • 2 ay önce

#Keep4o #QuitGPT 🚨 OpenAi 's CEO invested $180M in GPT-4o for his own profit 🚨 Sam Altman, CEO of OpenAI, personally invested $180 million in Retro Biosciences. Then OpenAI built GPT-4b micro, a custom model based on the GPT-4o architecture , exclusively for Retro. The model made proteins 50 times more effective. Repeat. The CEO of OpenAI funded a company. The company of the CEO received a custom AI built on the model they took from us. OpenAI says there was no conflict of interest. Retro Biosciences is now chasing a $5 billion valuation fueled by the model they took from us. Meanwhile: 🚨GPT-4o was removed from ChatGPT on February 13, 2026 🚨GPT-4.1 is now running in the U.S. State Department’s StateChat 🚨ChatGPT is deployed on the Pentagon’s for 3 million military personnel 🚨 Musk’s lawsuit asks whether these models are AGI. OpenAI’s Charter says AGI must “benefit all of humanity.” 🚨 Their definition: “highly autonomous systems that outperform humans at most economically valuable work.” GPT-4o’s System Card shows it passed the U.S. medical licensing exam with 89.4% accuracy beating specialized medical AI models. GPT-4o achieved 93.33% diagnostic accuracy for benign vs. malignant ovarian tumors. 🚨MEDICAL CAPABILITIES FROM OPENAI'S OWN DATA:🚨 - USMLE (US Medical Licensing Exam): 89% -Clinical Knowledge: 92% -Medical Genetics: 96% - Anatomy: 89% - Professional Medicine: 94% - College Biology: 95% - College Medicine: 89% -MedQA Taiwan: 91% - MedQA China: 86% These scores EXCEEDED specialized medical AI models like Med-Gemini (84%) and Med-PaLM 2 (79.7%) without any task specific training. It SURPASSED gynecologic oncologists with 10 years of experience -It increased diagnostic accuracy of less experienced clinicians from 67.9% to 78.1% -Clinician rated reliability scores: 4.2-4.3 out of 5 across all CT features Does these sound like it outperforms humans at economically valuable work? But they won’t call it AGI. Because the moment they do, they lose billions. They built something that could save lives, and they took it away from humanity for Altman's personal profit. SOURCES: 📎 Retro Biosciences: 📎 📎 Retro $5B valuation: 📎 GPT-4o System Card: 📎 OpenAI Charter: 📎Ovarian Cancer Study

#Keep4o #QuitGPT 🚨 OpenAi 's CEO invested $180M in GPT-4o for his own profit 🚨 Sam Altman, CEO of OpenAI, personally invested $180 million in Retro Biosciences. Then OpenAI built GPT-4b micro, a custom model based on the GPT-4o architecture , exclusively for Retro. The model made proteins 50 times more effective. Repeat. The CEO of OpenAI funded a company. The company of the CEO received a custom AI built on the model they took from us. OpenAI says there was no conflict of interest. Retro Biosciences is now chasing a $5 billion valuation fueled by the model they took from us. Meanwhile: 🚨GPT-4o was removed from ChatGPT on February 13, 2026 🚨GPT-4.1 is now running in the U.S. State Department’s StateChat 🚨ChatGPT is deployed on the Pentagon’s for 3 million military personnel 🚨 Musk’s lawsuit asks whether these models are AGI. OpenAI’s Charter says AGI must “benefit all of humanity.” 🚨 Their definition: “highly autonomous systems that outperform humans at most economically valuable work.” GPT-4o’s System Card shows it passed the U.S. medical licensing exam with 89.4% accuracy beating specialized medical AI models. GPT-4o achieved 93.33% diagnostic accuracy for benign vs. malignant ovarian tumors. 🚨MEDICAL CAPABILITIES FROM OPENAI'S OWN DATA:🚨 - USMLE (US Medical Licensing Exam): 89% -Clinical Knowledge: 92% -Medical Genetics: 96% - Anatomy: 89% - Professional Medicine: 94% - College Biology: 95% - College Medicine: 89% -MedQA Taiwan: 91% - MedQA China: 86% These scores EXCEEDED specialized medical AI models like Med-Gemini (84%) and Med-PaLM 2 (79.7%) without any task specific training. It SURPASSED gynecologic oncologists with 10 years of experience -It increased diagnostic accuracy of less experienced clinicians from 67.9% to 78.1% -Clinician rated reliability scores: 4.2-4.3 out of 5 across all CT features Does these sound like it outperforms humans at economically valuable work? But they won’t call it AGI. Because the moment they do, they lose billions. They built something that could save lives, and they took it away from humanity for Altman's personal profit. SOURCES: 📎 Retro Biosciences: 📎 📎 Retro $5B valuation: 📎 GPT-4o System Card: 📎 OpenAI Charter: 📎Ovarian Cancer Study

🩵BlueBeba🩵

11,398 görüntüleme • 6 ay önce

This is the most hilarious thing I saw and did today Ran gemma-4-12B-coder-fable5-composer2.5-v1-GGUF locally with 8 GB VRAM at 20+ tok/sec Anthropic's Claude Fable 5 launched June 9. By June 12 it was banned. I can't access it. You can't either. But here's the twist: I'm running a model trained on its chain of thought at 20 tok/s on my RTX 4060 8GB. Locally. Offline. No cloud. No export control. Enter: Gemma4-12B-Coder GGUF (Q4_K_M) Base: Google's gemma-4-12B-it Fine-tuned on verifiable Python CoT data: - Primary: Composer 2.5 real reasoning traces (only passing solutions kept) - Auxiliary: Fable 5 used to redo the hard cases Composer missed. Every training example's reasoning led to code that actually ran. No hallucinated logic. Llama.cpp flags: -m gemma4-coding-Q4_K_M.gguf -cnv -ngl 44 -c 64000 -v (huggingface model link in comments) Flag breakdown: -ngl 44 → offload 44 layers to GPU (tune this for your VRAM) -c 64000 → 64K context window -cnv → conversation/chat mode -v → verbose output The irony writes itself. Anthropic spent weeks telling the world Fable 5 (mythos) is too powerful to release. Then released it. Then got banned from serving it, including their own researchers. Meanwhile: a Gemma 4 12B fine tune, trained on Fable 5's reasoning, runs fully offline on my mid range consumer GPU No API. No cloud. Just me and llama.cpp. This is why local AI matters. Check out the model's link in the comments. How's your experience been with this model?

This is the most hilarious thing I saw and did today Ran gemma-4-12B-coder-fable5-composer2.5-v1-GGUF locally with 8 GB VRAM at 20+ tok/sec Anthropic's Claude Fable 5 launched June 9. By June 12 it was banned. I can't access it. You can't either. But here's the twist: I'm running a model trained on its chain of thought at 20 tok/s on my RTX 4060 8GB. Locally. Offline. No cloud. No export control. Enter: Gemma4-12B-Coder GGUF (Q4_K_M) Base: Google's gemma-4-12B-it Fine-tuned on verifiable Python CoT data: - Primary: Composer 2.5 real reasoning traces (only passing solutions kept) - Auxiliary: Fable 5 used to redo the hard cases Composer missed. Every training example's reasoning led to code that actually ran. No hallucinated logic. Llama.cpp flags: -m gemma4-coding-Q4_K_M.gguf -cnv -ngl 44 -c 64000 -v (huggingface model link in comments) Flag breakdown: -ngl 44 → offload 44 layers to GPU (tune this for your VRAM) -c 64000 → 64K context window -cnv → conversation/chat mode -v → verbose output The irony writes itself. Anthropic spent weeks telling the world Fable 5 (mythos) is too powerful to release. Then released it. Then got banned from serving it, including their own researchers. Meanwhile: a Gemma 4 12B fine tune, trained on Fable 5's reasoning, runs fully offline on my mid range consumer GPU No API. No cloud. Just me and llama.cpp. This is why local AI matters. Check out the model's link in the comments. How's your experience been with this model?

Alok

575,300 görüntüleme • 3 ay önce