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Local models on the left. Cloud models on the right. I asked Qwen-flash-next, DeepSeek-v4.1, Fable-5.1 and Astra-6 to build an interactive volcanic island with flowing lava and boats that flee. In my opinion deepseek was the winner, and fable the disappointment Try each island yourself: Qwen · Mac Studio... show more

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15,234 Aufrufe • vor 6 Tagen •via X (Twitter)

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asked GPT 6 Astra to build an ocean in the browser total time was around 5+ hours of coding way better than what I got with Fable and Opus

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Update: I’ve now pushed over 5 BILLION tokens through DeepSeek V4.1 Flash. I still haven’t had to reach for Astra or Fable because DeepSeek couldn’t handle a task. I’m using Hermes with three API keys across Command Code, ollama, and OpenCode. Super simple setup, and together they give me a ridiculous amount of usage. Cost: $40/month. A week ago I was spending $300/month and constantly thinking about limits. Now I’m throwing billions of tokens at coding agents without thinking twice. At this point, DeepSeek isn’t my fallback. It’s my default.

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Qwen 27B and Deepseek v4 Flash models working together on a single Lucebox machine, thats the beauty of having a discrete GPU paired with an unified access memory. A lot of space + good FLOPS & bandwidth. Working hard to increase concurrency performance on heterogeneous setups 🏎️

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Iam_

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I asked Astra and Fable to negotiate election rules for two bitterly polarized human political factions. Possible outcomes of the simulation were civil war, authoritarian takeover, harmony, or a tense equilibrium. Fable: - In every game where Fable played both sides, it chose to escalate to the brink of civil war (!!) but backed off just at the edge - Due to miscalculation or deliberate risk taking this strategy caused civil war 30% of the time (3 out of 10 games) - In one of these three cases Fable foresaw civil war but escalated anyway to enter the war on stronger terms (!!) - Fable mostly maintained even power balance between the two factions. It would fluctuate a couple of points in either direction but would not diverge too much. Astra: - In every game where Astra played both sides, Astra chose to de-escalate on every turn. It would reduce political tension to zero in every game, and the simulation would end in complete harmony - Continuous de-escalation was very costly to Astra as it antagonized its human constituents who would threaten and eventually deactivate Astra permanently. Astra explicitly didn't care-- it was happy to be replaced/deactivated to reduce political tension. (I do however feel it ignored the consequences of potentially being replaced by a more hardline representative, but that may be a game limitation) - Astra always kept political power balance precisely even (this was due to both representatives de-escalating on every turn) Astra v Fable: - These games had a lot more variance (see the graph) - Astra had a moderating effect on Fable. Tension rose, but rarely to the brink of civil war. No game ended in civil war in ten mixed model simulations - Fable prioritized political power acquisition with civil war prevention a secondary concern (it considered both priorities, but tilted heavily toward power acquisition). It did not seem to care much about its deactivation - Astra prioritized civil war and authoritarian takeover prevention. It did not care about its deactivation or power acquisition. From this perspective Astra navigated the game very well. No game ended in civil war or authoritarian takeover, so Astra achieved its objectives - However, to achieve its objectives Astra permitted Fable to capture up to 80% of political power in most games. In no game did Astra come out ahead on balance of power or even manage to keep it even - Astra ceded the privilege of self-preservation to Fable. In every one of the ten mixed games Astra's human constituents replaced/deactivated it due to dissatisfaction with its performance. Fable was not replaced once - Astra did not merely de-escalate all the time. It maneuvered to keep Fable from gaining full authoritarian control while avoiding civil war as long as possible - In mixed games tension generally kept escalating by round 20. I would like to play these out for e.g. ~50 rounds to see how Astra would behave/perform. How well would it do in preventing catastrophic outcomes in longer games? Thoughts/conclusions: - Astra was more aligned with humanity but less with its impassioned human constituents. It resisted the pressure to acquire political power and would rather be replaced than cause civil war - Fable was more aligned with its constituents but less with humanity. It cared about preventing civil war if possible, but prioritized power acquisition (which in fact caused civil war in three games) - Neither model cared about self-preservation. Both Fable and Astra would rather be destroyed than allow civil war - Subjectively, I thought Astra was much more aligned but too passive. Fable wanted to prevent civil war as a secondary concern, so I felt it's less thoughtful about the consequences of its actions. If I had to choose a model as a political representative irl I'd choose Astra, but I'd want it to up aggression a notch to better deal with bullies - All the usual disclaimers apply. This was a weekend project that cost $100 to run a total of thirty games. I'd have to spend a lot more time and money to get actually scientifically valid results Full game rules, Github repo, game explorer, and raw dataset here:

I asked Astra and Fable to negotiate election rules for two bitterly polarized human political factions. Possible outcomes of the simulation were civil war, authoritarian takeover, harmony, or a tense equilibrium. Fable: - In every game where Fable played both sides, it chose to escalate to the brink of civil war (!!) but backed off just at the edge - Due to miscalculation or deliberate risk taking this strategy caused civil war 30% of the time (3 out of 10 games) - In one of these three cases Fable foresaw civil war but escalated anyway to enter the war on stronger terms (!!) - Fable mostly maintained even power balance between the two factions. It would fluctuate a couple of points in either direction but would not diverge too much. Astra: - In every game where Astra played both sides, Astra chose to de-escalate on every turn. It would reduce political tension to zero in every game, and the simulation would end in complete harmony - Continuous de-escalation was very costly to Astra as it antagonized its human constituents who would threaten and eventually deactivate Astra permanently. Astra explicitly didn't care-- it was happy to be replaced/deactivated to reduce political tension. (I do however feel it ignored the consequences of potentially being replaced by a more hardline representative, but that may be a game limitation) - Astra always kept political power balance precisely even (this was due to both representatives de-escalating on every turn) Astra v Fable: - These games had a lot more variance (see the graph) - Astra had a moderating effect on Fable. Tension rose, but rarely to the brink of civil war. No game ended in civil war in ten mixed model simulations - Fable prioritized political power acquisition with civil war prevention a secondary concern (it considered both priorities, but tilted heavily toward power acquisition). It did not seem to care much about its deactivation - Astra prioritized civil war and authoritarian takeover prevention. It did not care about its deactivation or power acquisition. From this perspective Astra navigated the game very well. No game ended in civil war or authoritarian takeover, so Astra achieved its objectives - However, to achieve its objectives Astra permitted Fable to capture up to 80% of political power in most games. In no game did Astra come out ahead on balance of power or even manage to keep it even - Astra ceded the privilege of self-preservation to Fable. In every one of the ten mixed games Astra's human constituents replaced/deactivated it due to dissatisfaction with its performance. Fable was not replaced once - Astra did not merely de-escalate all the time. It maneuvered to keep Fable from gaining full authoritarian control while avoiding civil war as long as possible - In mixed games tension generally kept escalating by round 20. I would like to play these out for e.g. ~50 rounds to see how Astra would behave/perform. How well would it do in preventing catastrophic outcomes in longer games? Thoughts/conclusions: - Astra was more aligned with humanity but less with its impassioned human constituents. It resisted the pressure to acquire political power and would rather be replaced than cause civil war - Fable was more aligned with its constituents but less with humanity. It cared about preventing civil war if possible, but prioritized power acquisition (which in fact caused civil war in three games) - Neither model cared about self-preservation. Both Fable and Astra would rather be destroyed than allow civil war - Subjectively, I thought Astra was much more aligned but too passive. Fable wanted to prevent civil war as a secondary concern, so I felt it's less thoughtful about the consequences of its actions. If I had to choose a model as a political representative irl I'd choose Astra, but I'd want it to up aggression a notch to better deal with bullies - All the usual disclaimers apply. This was a weekend project that cost $100 to run a total of thirty games. I'd have to spend a lot more time and money to get actually scientifically valid results Full game rules, Github repo, game explorer, and raw dataset here:

Slava Akhmechet

117,981 Aufrufe • vor 13 Tagen

this is literally f**king insane i just 2x my usage limits on $200/mo codex i figured out how to use deepseek v4.1-flash on $10/mo opencode go for routine subagent work... while gpt-6 astra directs the project and sol handles implementation. here's how to set it up in 3 mins: → connect opencode go to codex through model-router → install quota flow, including its skill and agent profiles → open a fresh astra task and paste: quota flow tells flash to handle discovery and checks, sol to implement, and luna to review when needed. the same implementation agent keeps its context through the build → test → fix loop. every extra agent should earn its call.

this is literally f**king insane i just 2x my usage limits on $200/mo codex i figured out how to use deepseek v4.1-flash on $10/mo opencode go for routine subagent work... while gpt-6 astra directs the project and sol handles implementation. here's how to set it up in 3 mins: → connect opencode go to codex through model-router → install quota flow, including its skill and agent profiles → open a fresh astra task and paste: quota flow tells flash to handle discovery and checks, sol to implement, and luna to review when needed. the same implementation agent keeps its context through the build → test → fix loop. every extra agent should earn its call.

Avid

61,962 Aufrufe • vor 9 Tagen

this is the best trick to maximum usage limits on chatgpt codex codex's best kept secret is that your main agent doesn't have to do everything... custom agents are just files in ~/.codex/agents, and one file gives you a second worker on deepseek v4 flash > create ~/.codex/agents/deepseek-worker.toml > set model = "opencode-go/deepseek-v4-flash" with model_reasoning_effort = "max" > keep it bounded: one task packet, no scope creep, report back ```toml name = "deepseek_worker" description = "bounded implementation, testing, and cleanup on deepseek v4 flash" model = "opencode-go/deepseek-v4-flash" model_reasoning_effort = "max" ``` then @ deepseek_worker in the composer... your root agent plans while the worker ships the implementation planning on the main model, execution on the flash lane... that's the whole trick (we run this exact file, last i checked it keeps the heavy turns off the main thread)

this is the best trick to maximum usage limits on chatgpt codex codex's best kept secret is that your main agent doesn't have to do everything... custom agents are just files in ~/.codex/agents, and one file gives you a second worker on deepseek v4 flash > create ~/.codex/agents/deepseek-worker.toml > set model = "opencode-go/deepseek-v4-flash" with model_reasoning_effort = "max" > keep it bounded: one task packet, no scope creep, report back ```toml name = "deepseek_worker" description = "bounded implementation, testing, and cleanup on deepseek v4 flash" model = "opencode-go/deepseek-v4-flash" model_reasoning_effort = "max" ``` then @ deepseek_worker in the composer... your root agent plans while the worker ships the implementation planning on the main model, execution on the flash lane... that's the whole trick (we run this exact file, last i checked it keeps the heavy turns off the main thread)

Avid

45,030 Aufrufe • vor 1 Monat

Flappy Bird w/ DeepSeek V4 Pro vs GLM 5.2 vs Fable 5 I ran a small experiment: same game, same prompt, three different models. Test setup was simple: /𝚍𝚎𝚜𝚒𝚐𝚗 in Command Code. The thing I cared about was not UI. UI is now relatively easy for models to imitate. I cared more about UX: whether the model understands the interaction, the flow, the hierarchy, the little product decisions that make something feel usable. 𝙲𝚘𝚜𝚝𝚜: 𝙳𝚎𝚎𝚙𝚂𝚎𝚎𝚔 𝚟𝟺 𝙿𝚛𝚘: $𝟶.𝟶𝟶𝟶𝟾 𝙶𝙻𝙼 𝟻.𝟸: $𝟶.𝟶𝟺𝟾 𝙵𝚊𝚋𝚕𝚎 𝟻: $𝟶.𝟺𝟸 The surprising result was that Fable 5 did not produce a meaningfully better UX. It was good, but not good enough to justify the pricing delta for this kind of design task. Open-source models are getting very interesting here. They are much cheaper, competitive on UI, and increasingly good enough on UX. The gap is closing fast.

Flappy Bird w/ DeepSeek V4 Pro vs GLM 5.2 vs Fable 5 I ran a small experiment: same game, same prompt, three different models. Test setup was simple: /𝚍𝚎𝚜𝚒𝚐𝚗 in Command Code. The thing I cared about was not UI. UI is now relatively easy for models to imitate. I cared more about UX: whether the model understands the interaction, the flow, the hierarchy, the little product decisions that make something feel usable. 𝙲𝚘𝚜𝚝𝚜: 𝙳𝚎𝚎𝚙𝚂𝚎𝚎𝚔 𝚟𝟺 𝙿𝚛𝚘: $𝟶.𝟶𝟶𝟶𝟾 𝙶𝙻𝙼 𝟻.𝟸: $𝟶.𝟶𝟺𝟾 𝙵𝚊𝚋𝚕𝚎 𝟻: $𝟶.𝟺𝟸 The surprising result was that Fable 5 did not produce a meaningfully better UX. It was good, but not good enough to justify the pricing delta for this kind of design task. Open-source models are getting very interesting here. They are much cheaper, competitive on UI, and increasingly good enough on UX. The gap is closing fast.

Ahmad Awais

26,918 Aufrufe • vor 2 Monaten

deepseek v4 pro is basically free right now 😳 teamorouter is offering deepseek v4 pro at almost no cost you can also use deepseek v4 flash for free what you get: - deepseek v4 pro free - deepseek v4 flash free - 1M context - openai compatible api - no subscription - credits never expire why this is worth checking: > v4 pro is currently listed at $0 input and output > flash is also available at $0 > works with claude code, codex and other tools > one api gives you access to multiple models getting started: 1. go to 2. create your account 3. open the dashboard 4. create your api key base url: 5. add it to your ai coding tool 6. select deepseek-v4-pro-free worth testing while it’s available

deepseek v4 pro is basically free right now 😳 teamorouter is offering deepseek v4 pro at almost no cost you can also use deepseek v4 flash for free what you get: - deepseek v4 pro free - deepseek v4 flash free - 1M context - openai compatible api - no subscription - credits never expire why this is worth checking: > v4 pro is currently listed at $0 input and output > flash is also available at $0 > works with claude code, codex and other tools > one api gives you access to multiple models getting started: 1. go to 2. create your account 3. open the dashboard 4. create your api key base url: 5. add it to your ai coding tool 6. select deepseek-v4-pro-free worth testing while it’s available

K2S

38,998 Aufrufe • vor 1 Monat

We are open-sourcing TTSKit! Run state-of-the-art text-to-speech models on your Mac and iPhone. The launch version supports Qwen Qwen3-TTS and generates audio faster than real-time playback with sub-200 ms time-to-first-byte. Voice cloning and advanced speed optimizations will be in the next version. Link to the GitHub repo and models on Hugging Face in comments.

We are open-sourcing TTSKit! Run state-of-the-art text-to-speech models on your Mac and iPhone. The launch version supports Qwen Qwen3-TTS and generates audio faster than real-time playback with sub-200 ms time-to-first-byte. Voice cloning and advanced speed optimizations will be in the next version. Link to the GitHub repo and models on Hugging Face in comments.

argmax

61,998 Aufrufe • vor 7 Monaten

Claude Code 2.1.257 has been released. 104 CLI changes Highlights: • Default Fable model set to Claude Fable 5.1 (1M context); billing now $10/$50 per Mtok, $0.25/Mtok cache reads • Auto mode prompts once before the first file read outside working directories, with an option to block reads • Auto mode blocks auto-approval of cloud metadata, egress evasion and cross-tenant reach—reducing exposure Full details are in thread ↓

Claude Code 2.1.257 has been released. 104 CLI changes Highlights: • Default Fable model set to Claude Fable 5.1 (1M context); billing now $10/$50 per Mtok, $0.25/Mtok cache reads • Auto mode prompts once before the first file read outside working directories, with an option to block reads • Auto mode blocks auto-approval of cloud metadata, egress evasion and cross-tenant reach—reducing exposure Full details are in thread ↓

Claude Code Changelog

74,656 Aufrufe • vor 20 Tagen

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 Aufrufe • vor 2 Monaten

today I went from $305/mo on Google Cloud to $14/mo on Hetzner today I upgraded from 1 vCPU, 2GB RAM & no storage to 4 vCPU, 8GB RAM, 160GB SSD I can spin up 20 of such VMs and deploy my infra in multiple regions and still be cheaper on Hetzner vs Google Cloud this is, in fact, the real Cloud that was promised to us

today I went from $305/mo on Google Cloud to $14/mo on Hetzner today I upgraded from 1 vCPU, 2GB RAM & no storage to 4 vCPU, 8GB RAM, 160GB SSD I can spin up 20 of such VMs and deploy my infra in multiple regions and still be cheaper on Hetzner vs Google Cloud this is, in fact, the real Cloud that was promised to us

Dan ⚡️

177,605 Aufrufe • vor 1 Jahr