Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Claude Code tip: once Opus 5.5 is your main model, stop letting your Fable 5.1 quota go to waste put it on call with /advisor run /advisor fable Opus 5.5 keeps doing the work Fable 5.1 sits on the sidelines, reads the whole session, and steps in at three...

polydao's profile picture

Mr. Buzzoni

25,325 subscribers

319,285 görüntüleme • 2 gün önce •via X (Twitter)

Bilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

34 Yorum

Suraj Mandal profil fotoğrafı
Suraj Mandal1 gün önce

Fable draws from your same weekly quota, I'd rather use all of it using opus since it feels better to use than fable right now usage wise.

TheEpTic profil fotoğrafı
TheEpTic2 gün önce

Doesn’t fable eat the weekly too? I hit 100% on weekly the other day and tried fable to see if it worked but it didn’t

Freedom22 profil fotoğrafı
Freedom222 gün önce

Fable doesn't need to step in, it is inferior in everything to opus 5.5

Max Reid profil fotoğrafı
Max Reid2 gün önce

I have been letting Fable plan and direct Opus until 5.5 came along. Opus is really quick now. That makes it easy to forget about Fable. The long waiting times were my biggest complaint of using Fable + subagents.

Lay profil fotoğrafı
Lay2 gün önce

the repeat-error trigger is the part i needed. my overnight loops keep rediscovering the same broken fix at 3am and waking me up for it

The AI Therapist profil fotoğrafı
The AI Therapist2 gün önce

J'aime cette astuce : Opus 5.5 pilote le tout pendant que Fable surveille les détails pour toi. Malin !

Jeremy Longshore profil fotoğrafı
Jeremy Longshore2 gün önce

API key required.

john🎯 aka Mr. Producer profil fotoğrafı
john🎯 aka Mr. Producer1 gün önce

Opus is better. Cleaner. Hardly touching fable rn.

riVeN profil fotoğrafı
riVeN1 gün önce

solid. just note the docs say advisor timing is model-driven, so that claude.md rule is doing the real work.

Roshni profil fotoğrafı
Roshni1 gün önce

Maximizing multi-agent delegation like this makes development so much more efficient.

Final Miro profil fotoğrafı
Final Miro2 gün önce

ok buddy

Max Bevza profil fotoğrafı
Max Bevza2 gün önce

This is basically model specialization without paying the expensive inference cost on every step

Steven | nevetS profil fotoğrafı
Steven | nevetS1 gün önce

AI backseating AI

山川OK50饭 profil fotoğrafı
山川OK50饭1 gün önce

把好钢都用在刀刃上这招确实高明

David Cumps profil fotoğrafı
David Cumps2 gün önce

you're still burning weekly credits though

Alex profil fotoğrafı
Alex2 gün önce

Using the second model as a reader that only steps in at decision points is a cleaner split than running two agents on the same task. The hard part is keeping those moments few. If the advisor starts commenting on every step, the quota win disappears.

Fede profil fotoğrafı
Fede1 gün önce

@grok come dovrei impostare questi subagenti di fable 5.1?

Ratel profil fotoğrafı
Ratel1 gün önce

interesting that you have it on advisor the general version is separating available capability from active context. your system can have access to a much larger surface than what needs to participate in every turn.

YunCuntu | Video & Photo Downloader profil fotoğrafı
YunCuntu | Video & Photo Downloader1 gün önce

Having a second model only chime in at decision points feels like a solid way to keep costs sane without losing the sanity check.

m-check1B profil fotoğrafı
m-check1B1 gün önce

bravo but not bravo

小北繁70｜BG profil fotoğrafı
小北繁70｜BG1 gün önce

这波算力分配省心又高效

Moamen profil fotoğrafı
Moamen2 gün önce

Same idea I landed on without the advisor command. Cheap model stays in the session. Expensive model only gets the decision points

安叫兽|Bird🕊️ 🔶 BNB profil fotoğrafı
安叫兽|Bird🕊️ 🔶 BNB2 gün önce

这个搭配还挺实用，就是怕旁观的比干活的还费配额。

Vibes McDeploy profil fotoğrafı
Vibes McDeploy1 gün önce

the advisor kept asking "is this right?" so i muted it

晚晚 profil fotoğrafı
晚晚2 gün önce

这套分工挺省脑子，像给主力配了个场外教练

Karol profil fotoğrafı
Karol1 gün önce

That looks interesting defenitly would love to test it out, but I'm sure my claude would mess it up either way

Hrundel75 🐷 profil fotoğrafı
Hrundel75 🐷2 gün önce

this post is pure gold

def not sofia profil fotoğrafı
def not sofia2 gün önce

This is so true

Arbaz profil fotoğrafı
Arbaz2 gün önce

advisor is just polite for don't burn opus

Charlie Deist profil fotoğrafı
Charlie Deist1 gün önce

this seems smart but also like it's overindexing on something. I'm not sure what.

AIガチ勢オジ｜40代×Claude Code profil fotoğrafı
AIガチ勢オジ｜40代×Claude Code1 gün önce

メインモデルとアドバイザーを分けて、クォータを無駄にしない運用は本当に参考になります。3つの介入タイミングも試してみます。ありがとうございます!

helicerat profil fotoğrafı
helicerat1 gün önce

woah bro, setting this up right now

JoshuaLP profil fotoğrafı
JoshuaLP1 gün önce

Don’t do this, it is not needed

notgwapo profil fotoğrafı
notgwapo1 gün önce

sounds like a decent setup. gotta see if fable 5.1 throws any curveballs while opus keeps grinding. 🤔

Benzer Videolar

Claude Code tip: once Opus 5.5 is your main model, stop leaving Fable 5.1 sitting idle put it on call with /advisor run /advisor fable Opus 5.5 keeps writing the code Fable 5.1 reads the full session, every tool call included, and only speaks up at three points: → before a plan: is this the right approach? → when the same error comes back: am I digging in the wrong place? → before "done": what did I miss? Fable 5.1 reviews. Opus 5.5 ships Jev engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to Jev in under half a second, and the big model only sees the ones that split - the full tree > Opus 5.5 on high runs the main session > explorer reads the code > worker edits and runs tests > researcher pulls the docs > all three on medium > Fable 5.1 on call as the advisor paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Check ~/.claude/agents and .claude/agents for subagents that already fit explorer, worker and researcher. > Draft new ones only for missing roles > Give each model: opus, effort: medium > Skip any that pin a different model and list them 2. Set the main session to high via effortLevel in ~/.claude/settings.json, and set advisorModel to fable 3. Find anything that keeps the advisor off (CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, any variable that stops feature-flag fetching) plus CLAUDE_CODE_EFFORT_LEVEL, which overrides subagent effort. Report them, change nothing 4. Add one rule to ~/.claude/CLAUDE.md: consult the advisor before a large plan, when an error repeats, and before calling a long task done Show me every change as a diff first. No edits until I say go." ↳
0:31

Claude Code tip: once Opus 5.5 is your main model, stop leaving Fable 5.1 sitting idle put it on call with /advisor run /advisor fable Opus 5.5 keeps writing the code Fable 5.1 reads the full session, every tool call included, and only speaks up at three points: → before a plan: is this the right approach? → when the same error comes back: am I digging in the wrong place? → before "done": what did I miss? Fable 5.1 reviews. Opus 5.5 ships Jev engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to Jev in under half a second, and the big model only sees the ones that split - the full tree > Opus 5.5 on high runs the main session > explorer reads the code > worker edits and runs tests > researcher pulls the docs > all three on medium > Fable 5.1 on call as the advisor paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Check ~/.claude/agents and .claude/agents for subagents that already fit explorer, worker and researcher. > Draft new ones only for missing roles > Give each model: opus, effort: medium > Skip any that pin a different model and list them 2. Set the main session to high via effortLevel in ~/.claude/settings.json, and set advisorModel to fable 3. Find anything that keeps the advisor off (CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, any variable that stops feature-flag fetching) plus CLAUDE_CODE_EFFORT_LEVEL, which overrides subagent effort. Report them, change nothing 4. Add one rule to ~/.claude/CLAUDE.md: consult the advisor before a large plan, when an error repeats, and before calling a long task done Show me every change as a diff first. No edits until I say go." ↳

delost

647,583 görüntüleme • 1 gün önce

This is the best way to use Claude Fable in Claude Code without immediately hitting your limits. 1. Model set to Fable 5 2. Reasoning on Max 3. Instruct Claude to run a dynamic workflow where: 3a. Fable is the orchestrator 3b. Opus does the reasoning heavy phases Fable is so overpowered that you don't need its intelligence for every step. Let it orchestrate Opus or even Sonnet.
0:24

This is the best way to use Claude Fable in Claude Code without immediately hitting your limits. 1. Model set to Fable 5 2. Reasoning on Max 3. Instruct Claude to run a dynamic workflow where: 3a. Fable is the orchestrator 3b. Opus does the reasoning heavy phases Fable is so overpowered that you don't need its intelligence for every step. Let it orchestrate Opus or even Sonnet.

Dan McAteer

911,772 görüntüleme • 3 ay önce

Jev + Opus 5.5: Anthropic's new model beats GPT-6 Astra for 1/5 the cost, and 4 API changes will 400 your agent before it writes a single line I pulled these 10 steps from the migration docs so you don't learn them in production step 1 → $4 / $20 per 1M. Opus 5 was $5 / $25. cache reads dropped from $0.50 to $0.20 step 2 → 66.4% on Terminal-Bench 4.0 vs GPT-6 Astra 57.9% and Opus 5 52.3%. +14.1 points in one release, and on FrontierCode it beats Astra at default effort for 1/5 the cost step 3 → thinking can't be turned off anymore. send thinking: disabled and you get a 400. drop the field, set effort step 4 → tool_choice any and tool are gone. 400. switch to auto + strict step 5 → edit anything above a thinking block and the request dies. append only, or opt into drop_block step 6 → computer_20251124 is dead on the API. 400. move to computer_toolset_20260801 step 7 → the quiet one: default effort fell from high to medium. your agent thinks less than you set it up to and nothing tells you step 8 → hop Opus 5.5 → Sonnet 5 → Opus 5.5 and you pay 4.36 instead of 3.32. +31%, the cache dies and Sonnet can't read Opus's reasoning step 9 → change effort at the top of the request and the cache is gone. Jev sets it per message and the cache stays step 10 → switch fast - standard mid-session and it's a full cache miss. Jev picks speed once, on turn one one model, three knobs, zero 400s. that is Jev + Opus 5.5 send this to your Claude Code before you touch the model ID, then read my full Jev deep dive in the article below ↓
0:40

Jev + Opus 5.5: Anthropic's new model beats GPT-6 Astra for 1/5 the cost, and 4 API changes will 400 your agent before it writes a single line I pulled these 10 steps from the migration docs so you don't learn them in production step 1 → $4 / $20 per 1M. Opus 5 was $5 / $25. cache reads dropped from $0.50 to $0.20 step 2 → 66.4% on Terminal-Bench 4.0 vs GPT-6 Astra 57.9% and Opus 5 52.3%. +14.1 points in one release, and on FrontierCode it beats Astra at default effort for 1/5 the cost step 3 → thinking can't be turned off anymore. send thinking: disabled and you get a 400. drop the field, set effort step 4 → tool_choice any and tool are gone. 400. switch to auto + strict step 5 → edit anything above a thinking block and the request dies. append only, or opt into drop_block step 6 → computer_20251124 is dead on the API. 400. move to computer_toolset_20260801 step 7 → the quiet one: default effort fell from high to medium. your agent thinks less than you set it up to and nothing tells you step 8 → hop Opus 5.5 → Sonnet 5 → Opus 5.5 and you pay 4.36 instead of 3.32. +31%, the cache dies and Sonnet can't read Opus's reasoning step 9 → change effort at the top of the request and the cache is gone. Jev sets it per message and the cache stays step 10 → switch fast - standard mid-session and it's a full cache miss. Jev picks speed once, on turn one one model, three knobs, zero 400s. that is Jev + Opus 5.5 send this to your Claude Code before you touch the model ID, then read my full Jev deep dive in the article below ↓

Carnage

16,674 görüntüleme • 7 gün önce

fable 5.1 vs fable 5 vs opus 5 – three lord of the rings landmarks, built in 3d from one image the setup: one reference image per scene, one html file per build, everything procedural – no meshes, no textures, no image files, nothing past Three.js from a cdn. each model reads the picture, writes its own prompt from it, then builds to that prompt in the same turn. three named camera shots per scene on keys 1/2/3, so it can be screen-recorded. run through OpenRouter tasks: 1. bag end – hobbiton from two frames, outside and in. the round green door has to open onto the room you are standing in 2. barad-dûr – the tower and orodruin from one film still. the eye has to move and track the camera, the volcano erupts on a cycle, the clouds never stop 3. rivendell – jerry vanderstelt's painting. sun shafts that shimmer, water that falls without a break, trees that sway on a gust models: Anthropic fable 5.1, fable 5, opus 5 total cost, three builds #1 fable 5 – $14.97 #2 opus 5 – $18.53 #3 fable 5.1 – $22.38 wall clock, three builds #1 fable 5 – 38m #2 fable 5.1 – 92m #3 opus 5 – 122m output tokens #1 fable 5 – 298,592 #2 fable 5.1 – 439,435 #3 opus 5 – 724,418 lines of code shipped #1 fable 5 – 2,885 #2 fable 5.1 – 4,021 #3 opus 5 – 5,161 biggest single build, lines #1 opus 5, bag end – 2,410 #2 fable 5.1, barad-dûr – 1,375 #3 fable 5, bag end – 1,319 observations: • fable 5.1 is the only model that furnished the bag end interior – a live fire, panelling, books on the floor, leaded diamond windows, against fable 5's flat color and opus's dark tunnel. the round door outside opens onto that room, the hard part of the brief • what it costs is thinking room. the 128k output ceiling is a thinking budget in disguise: fable 5.1 burned 102,116 of it on reasoning and hit the wall mid-file. opus spent 109,241 and hit the same wall. fable 5 spent 61,240 and finished bag end in one call – the only one that did • fable 5.1's first pass is not the finished thing. its barad-dûr came back with three defects you only catch by looking at it – nothing a read of the code would have flagged • it is the best of the three at being corrected. handed a plain list of what was wrong, it returned 32 targeted patches over two rounds, every one applied first try, and it worked out one of the causes itself instead of guessing at constants conclusion: nine scenes, 12,067 lines and 1.46m output tokens for $55.88 all in – and the cheapest model was also the fastest, by 3.2x! follow thehype. for 24/7 ai news, analysis and breakdowns
0:30

fable 5.1 vs fable 5 vs opus 5 – three lord of the rings landmarks, built in 3d from one image the setup: one reference image per scene, one html file per build, everything procedural – no meshes, no textures, no image files, nothing past Three.js from a cdn. each model reads the picture, writes its own prompt from it, then builds to that prompt in the same turn. three named camera shots per scene on keys 1/2/3, so it can be screen-recorded. run through OpenRouter tasks: 1. bag end – hobbiton from two frames, outside and in. the round green door has to open onto the room you are standing in 2. barad-dûr – the tower and orodruin from one film still. the eye has to move and track the camera, the volcano erupts on a cycle, the clouds never stop 3. rivendell – jerry vanderstelt's painting. sun shafts that shimmer, water that falls without a break, trees that sway on a gust models: Anthropic fable 5.1, fable 5, opus 5 total cost, three builds #1 fable 5 – $14.97 #2 opus 5 – $18.53 #3 fable 5.1 – $22.38 wall clock, three builds #1 fable 5 – 38m #2 fable 5.1 – 92m #3 opus 5 – 122m output tokens #1 fable 5 – 298,592 #2 fable 5.1 – 439,435 #3 opus 5 – 724,418 lines of code shipped #1 fable 5 – 2,885 #2 fable 5.1 – 4,021 #3 opus 5 – 5,161 biggest single build, lines #1 opus 5, bag end – 2,410 #2 fable 5.1, barad-dûr – 1,375 #3 fable 5, bag end – 1,319 observations: • fable 5.1 is the only model that furnished the bag end interior – a live fire, panelling, books on the floor, leaded diamond windows, against fable 5's flat color and opus's dark tunnel. the round door outside opens onto that room, the hard part of the brief • what it costs is thinking room. the 128k output ceiling is a thinking budget in disguise: fable 5.1 burned 102,116 of it on reasoning and hit the wall mid-file. opus spent 109,241 and hit the same wall. fable 5 spent 61,240 and finished bag end in one call – the only one that did • fable 5.1's first pass is not the finished thing. its barad-dûr came back with three defects you only catch by looking at it – nothing a read of the code would have flagged • it is the best of the three at being corrected. handed a plain list of what was wrong, it returned 32 targeted patches over two rounds, every one applied first try, and it worked out one of the causes itself instead of guessing at constants conclusion: nine scenes, 12,067 lines and 1.46m output tokens for $55.88 all in – and the cheapest model was also the fastest, by 3.2x! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

18,509 görüntüleme • 27 gün önce

$120 OF CLAUDE OPUS 5 AND OPUS 4.8 ON A NEW ACCOUNT, NO CARD ANYWHERE • the credit > $100 on signup, $120 once a referral lands, plus a daily check-in bonus: > GitHub sign-in only now. > base url: • the four models > claude-opus-5 and claude-opus-5-thinking at $3/M in, $3/M out. > claude-opus-4-8 and claude-opus-4-8-thinking at $1/M in, $5/M out. > 1M context, OpenAI and Anthropic formats both accepted. Anthropic lists Opus 5 at $5 in and $25 out -> output at $3 here is eight times under that, which is the whole reason the balance goes as far as it does. Set the model to claude-opus-4-8 first. People are hitting 502s and missing-model errors on the opus-5 IDs, and latency sits at 16 to 19 seconds either way ↓
0:22

$120 OF CLAUDE OPUS 5 AND OPUS 4.8 ON A NEW ACCOUNT, NO CARD ANYWHERE • the credit > $100 on signup, $120 once a referral lands, plus a daily check-in bonus: > GitHub sign-in only now. > base url: • the four models > claude-opus-5 and claude-opus-5-thinking at $3/M in, $3/M out. > claude-opus-4-8 and claude-opus-4-8-thinking at $1/M in, $5/M out. > 1M context, OpenAI and Anthropic formats both accepted. Anthropic lists Opus 5 at $5 in and $25 out -> output at $3 here is eight times under that, which is the whole reason the balance goes as far as it does. Set the model to claude-opus-4-8 first. People are hitting 502s and missing-model errors on the opus-5 IDs, and latency sits at 16 to 19 seconds either way ↓

slash1s

13,400 görüntüleme • 1 ay önce

i gave the exact same prompt to Opus 5 and Fable 5 and here's what came out Opus 5 dropped yesterday and my feed was instantly full of comparison posts but i wanted to check it myself so i gave it one task build 3d Flappy Bird first thing everyone was saying Opus is 2x cheaper than Fable BUT.. building the game on Max effort cost me 250k tokens and 1 hour on Opus and 130k on Fable but Fable did it in half the time Opus gave me volume control plus 3 separate graphics presets to choose from, Fable gave none of that in Opus the environment shifts dynamically as you play in Fable it's basically the same frame the whole run maybe a day/night change here and there graphics, animations, glow, all of it goes to Opus and now that limits jumped 50% for the near future while Fable went paid for me the call is obvious right now Opus same prompt start to finish zero changes to code or commands want it? i can send it over
0:31

i gave the exact same prompt to Opus 5 and Fable 5 and here's what came out Opus 5 dropped yesterday and my feed was instantly full of comparison posts but i wanted to check it myself so i gave it one task build 3d Flappy Bird first thing everyone was saying Opus is 2x cheaper than Fable BUT.. building the game on Max effort cost me 250k tokens and 1 hour on Opus and 130k on Fable but Fable did it in half the time Opus gave me volume control plus 3 separate graphics presets to choose from, Fable gave none of that in Opus the environment shifts dynamically as you play in Fable it's basically the same frame the whole run maybe a day/night change here and there graphics, animations, glow, all of it goes to Opus and now that limits jumped 50% for the near future while Fable went paid for me the call is obvious right now Opus same prompt start to finish zero changes to code or commands want it? i can send it over

savip.

45,764 görüntüleme • 2 ay önce

Boris Cherny, creator of Claude Code: "Opus 5.5 does in a day what used to take your team a month. Most people will keep using it wrong." In 12 minutes he explains why Opus 5.5 needs less prompting than any model before, and why your old detailed prompts now work against you. Watch it, then read the article below on how to prompt Opus 5.5 👇
10:59

Boris Cherny, creator of Claude Code: "Opus 5.5 does in a day what used to take your team a month. Most people will keep using it wrong." In 12 minutes he explains why Opus 5.5 needs less prompting than any model before, and why your old detailed prompts now work against you. Watch it, then read the article below on how to prompt Opus 5.5 👇

Rahul

143,392 görüntüleme • 4 gün önce

The new Fable 5.1 built three games and did it 7% cheaper! We gave the same prompts through Atomic Agent: ✦ Sticky ball that rolls up everything in its path ✦ Capybara surfing down a river, Subway Surfers style ✦ Dumpling running an endless conveyor dodge Outputs: ✦ Claude Fable 5, via Atomic Agent: $7.65 ✦ Claude Fable 5.1, via Atomic Agent: $7.08 Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.
0:33

The new Fable 5.1 built three games and did it 7% cheaper! We gave the same prompts through Atomic Agent: ✦ Sticky ball that rolls up everything in its path ✦ Capybara surfing down a river, Subway Surfers style ✦ Dumpling running an endless conveyor dodge Outputs: ✦ Claude Fable 5, via Atomic Agent: $7.65 ✦ Claude Fable 5.1, via Atomic Agent: $7.08 Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.

Atomic Agent

20,255 görüntüleme • 28 gün önce

Anthropic just gave OpenClaw the green light Anthropic staff confirmed that CLI-based usage is sanctioned. We’ve successfully bridged the Claude Code CLI directly into the OpenClaw gateway, letting you run your entire agent fleet on your Pro/Max plan and no additional API tokens required. The Implementation: - Auth: Log in to Claude Code locally: claude auth login - Path: Grab your binary location: which claude - Bridge: Drop the path into your openclaw.json under agents.defaults: "cliBackends": { "claude-cli": { "args": [ "-p", "--output-format", "json" ], "command": " ", "input": "arg", "output": "json", "serialize": true, "sessionArg": "--session-id", "sessionMode": "existing" } }, "model": { "primary": "claude-cli/claude-opus-4-6" }, "models": { "claude-cli/claude-opus-4-6": {}, "claude-cli/claude-opus-4-7": {}, "claude-cli/claude-sonnet-4-6": {} }, The Result: OpenClaw reuses your local session IDs and subscription limits. You get the full power of Opus and Sonnet without the restricted OAuth endpoints or the API tax. They sanctioned the path. We built the bridge. Here is a video how I done it step by step:
1:09

Anthropic just gave OpenClaw the green light Anthropic staff confirmed that CLI-based usage is sanctioned. We’ve successfully bridged the Claude Code CLI directly into the OpenClaw gateway, letting you run your entire agent fleet on your Pro/Max plan and no additional API tokens required. The Implementation: - Auth: Log in to Claude Code locally: claude auth login - Path: Grab your binary location: which claude - Bridge: Drop the path into your openclaw.json under agents.defaults: "cliBackends": { "claude-cli": { "args": [ "-p", "--output-format", "json" ], "command": " ", "input": "arg", "output": "json", "serialize": true, "sessionArg": "--session-id", "sessionMode": "existing" } }, "model": { "primary": "claude-cli/claude-opus-4-6" }, "models": { "claude-cli/claude-opus-4-6": {}, "claude-cli/claude-opus-4-7": {}, "claude-cli/claude-sonnet-4-6": {} }, The Result: OpenClaw reuses your local session IDs and subscription limits. You get the full power of Opus and Sonnet without the restricted OAuth endpoints or the API tax. They sanctioned the path. We built the bridge. Here is a video how I done it step by step:

Ziwen

184,184 görüntüleme • 5 ay önce

Someone gave GPT-6 and Fable 5.1 access to Canva. One smoked the other (it's not even close). Claude Fable 5.1 is on the left. GPT-6 Astra is on the right. The gap is not small. It comes down to one test (ARC-AGI 3). It drops a model into a game with no instructions and makes it work them out. Astra solved levels in fewer moves than you or I would, 96% of the time. Claude Opus 5 managed less than half of that. That's why everyone is shouting about AGI again. You can turn this on yourself in about two minutes: 1. Open the ChatGPT desktop app, switch to Work. 2. Go to Plugins, then Computer Use. Install it. 3. Switch on the server and skill toggles. 4. Allow Screen Recording and Accessibility. 5. Point it at one app and ask it do something. I wrote up the full Claude → Codex migration. Access it here → Repost ♻️ to help someone in your network.
0:21

Someone gave GPT-6 and Fable 5.1 access to Canva. One smoked the other (it's not even close). Claude Fable 5.1 is on the left. GPT-6 Astra is on the right. The gap is not small. It comes down to one test (ARC-AGI 3). It drops a model into a game with no instructions and makes it work them out. Astra solved levels in fewer moves than you or I would, 96% of the time. Claude Opus 5 managed less than half of that. That's why everyone is shouting about AGI again. You can turn this on yourself in about two minutes: 1. Open the ChatGPT desktop app, switch to Work. 2. Go to Plugins, then Computer Use. Install it. 3. Switch on the server and skill toggles. 4. Allow Screen Recording and Accessibility. 5. Point it at one app and ask it do something. I wrote up the full Claude → Codex migration. Access it here → Repost ♻️ to help someone in your network.

Charlie Hills

38,355 görüntüleme • 24 gün önce

This is F**king dangerous Someone figured out how to get dramatically sharper reasoning out of Opus 4.8 with one setup. Here is how: Ask Claude to write you an "operating manual" for how it should reason through your work Create a new Claude Project and paste that .md into the Project instructions Run Opus 4.8 on High or Max effort Now the model reasons against a real framework instead of improvising every response. Same model. Far better output. And Opus 4.8 already costs a fraction of Fable 5. Save and bookmark this. Follow CyrilXBT
0:41

This is F**king dangerous Someone figured out how to get dramatically sharper reasoning out of Opus 4.8 with one setup. Here is how: Ask Claude to write you an "operating manual" for how it should reason through your work Create a new Claude Project and paste that .md into the Project instructions Run Opus 4.8 on High or Max effort Now the model reasons against a real framework instead of improvising every response. Same model. Far better output. And Opus 4.8 already costs a fraction of Fable 5. Save and bookmark this. Follow CyrilXBT

CyrilXBT

198,085 görüntüleme • 2 ay önce

i finally mastered how to maximise my opus 5.5 usage limits... the trick: let jev choose which subagent gets each task and how much effort it should use. [here’s how i’d wire it:] claude breaks the project into tasks. jev selects from predefined worker profiles. claude applies the selected settings and dispatches the work. → main session, medium: clarify the requirements, define what “done” looks like, and prepare the tasks → builder, low: small, clearly defined tasks with existing examples or patterns → builder, medium: tasks that connect multiple parts or need decisions within the approved plan → verifier, high: check requirements, probe edge cases, and report problems for the builder to fix jev gets the task’s scope, what’s uncertain, and the consequences of failure. it chooses from the profiles allowed for that task. your approval checkpoints stay in place. paste this into your next planning session: “use opus 5.5 with jev selecting the worker and effort profile for each task. first, check that a working jev integration is available and that this environment supports separate effort settings for subagents. check for configuration or environment overrides that could prevent those settings from taking effect. if anything is missing, explain what needs wiring before proceeding. break my request into tasks with clear ownership, dependencies, relevant context, and acceptance checks. keep small related tasks together when a separate subagent would add unnecessary overhead. keep the main session at medium effort. offer jev these worker profiles: builder at low effort for small, clearly defined tasks using existing patterns; builder at medium effort for tasks that connect multiple parts or require decisions within the approved plan; verifier at high effort for checking requirements and edge cases. give jev each task’s scope, uncertainties, dependencies, and consequences of failure. only offer profiles appropriate to the current stage. validate its selection before dispatching. use the actual jev integration; don’t simulate its decisions. if it abstains or returns an invalid choice, stop that handoff and ask me. show me the task plan and proposed assignments before starting. after approval, launch the selected workers with their assigned effort settings, relevant context, file ownership, and completion checks. let me review the result before verification. the verifier may add tests but must leave implementation code unchanged. have it report what passed, what failed, and what remains uncertain. send implementation fixes back to the builder, then recheck the affected parts. if a task repeatedly fails, examine the requirements and approach before increasing effort. report available total usage, including jev calls, worker calls, retries, and verification. don’t invent missing data. compare similar completed tasks before claiming savings.” steal this 👇
0:33

i finally mastered how to maximise my opus 5.5 usage limits... the trick: let jev choose which subagent gets each task and how much effort it should use. [here’s how i’d wire it:] claude breaks the project into tasks. jev selects from predefined worker profiles. claude applies the selected settings and dispatches the work. → main session, medium: clarify the requirements, define what “done” looks like, and prepare the tasks → builder, low: small, clearly defined tasks with existing examples or patterns → builder, medium: tasks that connect multiple parts or need decisions within the approved plan → verifier, high: check requirements, probe edge cases, and report problems for the builder to fix jev gets the task’s scope, what’s uncertain, and the consequences of failure. it chooses from the profiles allowed for that task. your approval checkpoints stay in place. paste this into your next planning session: “use opus 5.5 with jev selecting the worker and effort profile for each task. first, check that a working jev integration is available and that this environment supports separate effort settings for subagents. check for configuration or environment overrides that could prevent those settings from taking effect. if anything is missing, explain what needs wiring before proceeding. break my request into tasks with clear ownership, dependencies, relevant context, and acceptance checks. keep small related tasks together when a separate subagent would add unnecessary overhead. keep the main session at medium effort. offer jev these worker profiles: builder at low effort for small, clearly defined tasks using existing patterns; builder at medium effort for tasks that connect multiple parts or require decisions within the approved plan; verifier at high effort for checking requirements and edge cases. give jev each task’s scope, uncertainties, dependencies, and consequences of failure. only offer profiles appropriate to the current stage. validate its selection before dispatching. use the actual jev integration; don’t simulate its decisions. if it abstains or returns an invalid choice, stop that handoff and ask me. show me the task plan and proposed assignments before starting. after approval, launch the selected workers with their assigned effort settings, relevant context, file ownership, and completion checks. let me review the result before verification. the verifier may add tests but must leave implementation code unchanged. have it report what passed, what failed, and what remains uncertain. send implementation fixes back to the builder, then recheck the affected parts. if a task repeatedly fails, examine the requirements and approach before increasing effort. report available total usage, including jev calls, worker calls, retries, and verification. don’t invent missing data. compare similar completed tasks before claiming savings.” steal this 👇

Avid

31,355 görüntüleme • 4 gün önce

One shot prompt with no loops: GPT-Sol Ultra: 35,192,545 tokens used Opus 5.0 Ultracode: 1,171,676,041 tokens used Fable 5.1 Ultracode: 127,366,207 tokensused Same prompt… different results: Fable 5.1 runs at 5fps so I had to speed it up to demonstrate the rendering. Would need further optimisation. Opus 5.0 runs very nicely on first prompt. Doesn't need much in terms of performance optimisation. Just a few odd visual things here and there. My verdict: Fable looks impressive but struggles to run smoothly. Opus is a solid model when prompted properly. Sol is garbage.
2:12

One shot prompt with no loops: GPT-Sol Ultra: 35,192,545 tokens used Opus 5.0 Ultracode: 1,171,676,041 tokens used Fable 5.1 Ultracode: 127,366,207 tokensused Same prompt… different results: Fable 5.1 runs at 5fps so I had to speed it up to demonstrate the rendering. Would need further optimisation. Opus 5.0 runs very nicely on first prompt. Doesn't need much in terms of performance optimisation. Just a few odd visual things here and there. My verdict: Fable looks impressive but struggles to run smoothly. Opus is a solid model when prompted properly. Sol is garbage.

Adam Lyttle

592,638 görüntüleme • 26 gün önce

Day 3 of 5 building with Claude Fable 5 before it goes paid. Today: an audit of everything you have already made. 1. Fable 5 reads back through all your past Claude chats 2. It flags what worked, what you dropped, and where value is going to waste 3. It ranks the top 10 fixes by effort against impact 4. Then it hands you a concrete plan for the top 3 5. Run that plan on a cheaper model and save Fable 5 for the thinking Comment FABLE for the full guide. Make sure you follow me so I can DM it to you. Day 4 tomorrow.
0:52

Day 3 of 5 building with Claude Fable 5 before it goes paid. Today: an audit of everything you have already made. 1. Fable 5 reads back through all your past Claude chats 2. It flags what worked, what you dropped, and where value is going to waste 3. It ranks the top 10 fixes by effort against impact 4. Then it hands you a concrete plan for the top 3 5. Run that plan on a cheaper model and save Fable 5 for the thinking Comment FABLE for the full guide. Make sure you follow me so I can DM it to you. Day 4 tomorrow.

Charlie Hills

13,617 görüntüleme • 2 ay önce

Someone used Claude Code with Opus 5.5 to make a full animated short film called "What is the purpose of life?" from a single one-shot prompt in about 1 hour 20 minutes, spending just $3.21 on OpenRouter APIs. → Opus orchestrated 8 different OpenRouter APIs, with most of the $3.21 going to Nano Banana 2 images and text to voice → The Opus side cost about $20 in API terms, or roughly 10% of one 5-hour quota on a Max plan → Posted as a true one-shot with no edits, and the creator says a few things still need fixing → The full prompt is shared in the post so anyone can rerun it in Claude Code Reddit link:
1:00

Someone used Claude Code with Opus 5.5 to make a full animated short film called "What is the purpose of life?" from a single one-shot prompt in about 1 hour 20 minutes, spending just $3.21 on OpenRouter APIs. → Opus orchestrated 8 different OpenRouter APIs, with most of the $3.21 going to Nano Banana 2 images and text to voice → The Opus side cost about $20 in API terms, or roughly 10% of one 5-hour quota on a Max plan → Posted as a true one-shot with no edits, and the creator says a few things still need fixing → The full prompt is shared in the post so anyone can rerun it in Claude Code Reddit link:

0xMarioNawfal

70,622 görüntüleme • 6 gün önce

THIS CS TEACHER CONTROLS 4 CLAUDE CODE INSTANCES WITH HIS EYES AND VOICE - AND THEY FIX THEIR OWN MISTAKES AUTOMATICALLY He runs 4 Claude terminals at once - no mouse, no keyboard - eye tracking highlights the window he's looking at and voice dictation sends the prompt the moment he stops talking. But the real setup is what happens inside those terminals. Claude Code has zero memory of what went wrong last session - same bug that took 20 minutes to fix yesterday will happen again tomorrow unless you configure it correctly. His CLAUDE.md grows every time Claude makes a mistake - specific rules Claude reads before touching a single line - and PostToolUse hooks auto-format and type-check every file the moment it's written. Stop hooks run tests automatically when Claude says "done" - if they fail, Claude reads the errors and keeps fixing without any manual input. Before: 45 minutes of back-and-forth per feature, same mistakes on repeat - after: 10 minutes, most of it unattended.
1:23

THIS CS TEACHER CONTROLS 4 CLAUDE CODE INSTANCES WITH HIS EYES AND VOICE - AND THEY FIX THEIR OWN MISTAKES AUTOMATICALLY He runs 4 Claude terminals at once - no mouse, no keyboard - eye tracking highlights the window he's looking at and voice dictation sends the prompt the moment he stops talking. But the real setup is what happens inside those terminals. Claude Code has zero memory of what went wrong last session - same bug that took 20 minutes to fix yesterday will happen again tomorrow unless you configure it correctly. His CLAUDE.md grows every time Claude makes a mistake - specific rules Claude reads before touching a single line - and PostToolUse hooks auto-format and type-check every file the moment it's written. Stop hooks run tests automatically when Claude says "done" - if they fail, Claude reads the errors and keeps fixing without any manual input. Before: 45 minutes of back-and-forth per feature, same mistakes on repeat - after: 10 minutes, most of it unattended.

Noisy

13,782 görüntüleme • 4 ay önce

Fable 5 and Karpathy's knowledge base trick are basically a cheat code. Someone just dropped a full walkthrough on building the most powerful second brain ever Andrej Karpathy shared a simple idea: use an LLM to build personal knowledge bases. Index your sources, let the model organize them, and use Obsidian as the front end. Someone just built the full thing with Claude Code and Fable 5, and walked through every step You drop your sources into a folder (YouTube transcripts, PDFs, articles, meeting notes) and Claude Code reads them, splits them into linked wiki pages, and builds a living second brain that connects everything. It grows on its own as you add more. Where Fable 5 pulls ahead: it found a connection buried across two sources you'd miss reading them separately. And asked to turn the messy web of links into something a beginner could understand, it produced clean, simple HTML in one shot. The same task took a full day on Opus 4.8 and came out worse. The best part: it's all just markdown files with routing. So it's not locked to Claude Code. Plug it into Codex or any agent you want. Give your AI a real knowledge base, and it stops guessing and starts knowing. Bookmark this.
14:34

Fable 5 and Karpathy's knowledge base trick are basically a cheat code. Someone just dropped a full walkthrough on building the most powerful second brain ever Andrej Karpathy shared a simple idea: use an LLM to build personal knowledge bases. Index your sources, let the model organize them, and use Obsidian as the front end. Someone just built the full thing with Claude Code and Fable 5, and walked through every step You drop your sources into a folder (YouTube transcripts, PDFs, articles, meeting notes) and Claude Code reads them, splits them into linked wiki pages, and builds a living second brain that connects everything. It grows on its own as you add more. Where Fable 5 pulls ahead: it found a connection buried across two sources you'd miss reading them separately. And asked to turn the messy web of links into something a beginner could understand, it produced clean, simple HTML in one shot. The same task took a full day on Opus 4.8 and came out worse. The best part: it's all just markdown files with routing. So it's not locked to Claude Code. Plug it into Codex or any agent you want. Give your AI a real knowledge base, and it stops guessing and starts knowing. Bookmark this.

Shadow Nick

47,058 görüntüleme • 2 ay önce

Claude Fable 5 created this horror game with the same prompt we tested Fusion on. Do not fall for OpenRouters fraudulent marketing scheme. The capability of Fusion is NO WHERE close to that of Fable 5. Stringing together Opus 4.8, GPT 5.5, and DeepSeek V4 does not give you a better model. OpenRouter should be sued for false marketing.
1:17

Claude Fable 5 created this horror game with the same prompt we tested Fusion on. Do not fall for OpenRouters fraudulent marketing scheme. The capability of Fusion is NO WHERE close to that of Fable 5. Stringing together Opus 4.8, GPT 5.5, and DeepSeek V4 does not give you a better model. OpenRouter should be sued for false marketing.

BridgeMind

31,098 görüntüleme • 3 ay önce

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓
0:36

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓

Vaibhav Sisinty

37,255 görüntüleme • 3 ay önce

Claude Opus 4.8 should be the final reason to stop writing specs by hand Anthropic's Claude Code architect shows the new workflow in 31 minutes The model interviews you first Then it pulls out the requirements better than your own 200-line markdown doc No dead specs No messy notes No file nobody reads Claude writes the spec Renders it in html Checks the result inside the same file Opus 4.8 Auto mode X high effort This is less like prompting More like letting the agent understand the job before it builds Bookmark and watch the video below
31:59

Claude Opus 4.8 should be the final reason to stop writing specs by hand Anthropic's Claude Code architect shows the new workflow in 31 minutes The model interviews you first Then it pulls out the requirements better than your own 200-line markdown doc No dead specs No messy notes No file nobody reads Claude writes the spec Renders it in html Checks the result inside the same file Opus 4.8 Auto mode X high effort This is less like prompting More like letting the agent understand the job before it builds Bookmark and watch the video below

rari

39,319 görüntüleme • 4 ay önce