Darren Neese, Microsoft Power Apps MVP retweeted
Humanoid robot market map as of June 2026.
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RT @levelsio: Remembering the 3 days I had access to Fable
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Darren Neese, Microsoft Power Apps MVP retweeted
Introducing the Fusion API, the smartest compound model in the market.
Fusion achieves Fable-level intelligence at half the price.
How it works 👇
Fusion achieves Fable-level intelligence at half the price.
How it works 👇
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Darren Neese, Microsoft Power Apps MVP retweeted
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: anthropic.com/news/fable-myt…
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: anthropic.com/news/fable-myt…
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Darren Neese, Microsoft Power Apps MVP retweeted
We've reset 5-hour and weekly rate limits for all users. Enjoy Fable 5!
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Darren Neese, Microsoft Power Apps MVP retweeted
Claude's first day at Dunder Mifflin
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Darren Neese, Microsoft Power Apps MVP retweeted
Andrej Karpathy: "90% of your AI coding bill is paying for context you didn't need to send"
Here are 10 things senior AI engineers stopped wasting tokens on:
1. Auto-context loading 50 files for a 30-line fix: $1.20/turn for tokens you'll never read. 80% input waste, every s on lint, format, and rename tasks: $0.60 for what Haiku nails at $0.02. 30x overpay on the cleanup tier
3. Tool call loops that re-send the full repo on every retry: 5x context cost per agentic flow. fixing these alone cuts 30-50% of bills
4. Sonnet as the default model: Kimi 2.6 matches its quality on most coding tasks at 1/6 the cost. defaulting to Sonnet in 2026 is leaving 60-70% on the table
5. Streaming responses on stable-prefix workflows: kills your prompt cache. you pay 10x for tokens that should have cost cents
6. "Just in case" file includes: 80,000-token prompts that should be 3,000. context bloat is the silent budget killer
7. Per-session knowledge rebuilding: 10 min writing a SKILL.md once vs paying agents to re-figure out your environment every run. $4 vs $0.30 per execution
8. Single-model setups: premium tier on every task is the most expensive mistake in AI coding right now
9. Asking 10 small questions one at a time: 10 separate input prefix charges vs one batched call. 70-90% savings on routine workflows
10. Buying Claude Pro + ChatGPT Plus + Cursor Pro: you seriously use one. the other two are habit, not utility
what actually compounds instead:
- context discipline (grep before fetching, always)
- prompt caching on every stable prefix
- multi-model routing (Kimi 2.6 default, Opus for the 10%)
- graduated skills via SKILL.md files
- profiling tool calls before optimizing prompts
- the routing mindset (right model for right task)
in 12 months, the gap between developers shipping on $200/month and $4,000/month budgets won't be skill
it'll be how well they route
study this.
Here are 10 things senior AI engineers stopped wasting tokens on:
1. Auto-context loading 50 files for a 30-line fix: $1.20/turn for tokens you'll never read. 80% input waste, every s on lint, format, and rename tasks: $0.60 for what Haiku nails at $0.02. 30x overpay on the cleanup tier
3. Tool call loops that re-send the full repo on every retry: 5x context cost per agentic flow. fixing these alone cuts 30-50% of bills
4. Sonnet as the default model: Kimi 2.6 matches its quality on most coding tasks at 1/6 the cost. defaulting to Sonnet in 2026 is leaving 60-70% on the table
5. Streaming responses on stable-prefix workflows: kills your prompt cache. you pay 10x for tokens that should have cost cents
6. "Just in case" file includes: 80,000-token prompts that should be 3,000. context bloat is the silent budget killer
7. Per-session knowledge rebuilding: 10 min writing a SKILL.md once vs paying agents to re-figure out your environment every run. $4 vs $0.30 per execution
8. Single-model setups: premium tier on every task is the most expensive mistake in AI coding right now
9. Asking 10 small questions one at a time: 10 separate input prefix charges vs one batched call. 70-90% savings on routine workflows
10. Buying Claude Pro + ChatGPT Plus + Cursor Pro: you seriously use one. the other two are habit, not utility
what actually compounds instead:
- context discipline (grep before fetching, always)
- prompt caching on every stable prefix
- multi-model routing (Kimi 2.6 default, Opus for the 10%)
- graduated skills via SKILL.md files
- profiling tool calls before optimizing prompts
- the routing mindset (right model for right task)
in 12 months, the gap between developers shipping on $200/month and $4,000/month budgets won't be skill
it'll be how well they route
study this.
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