So Anthropic has made Fable degrade based on what you ask. Seems like a good approach for blocking capabilities on certain topics, but... DID NO ONE THINK THAT THE MODEL COULD LEARN FROM THIS??? seriously wtf!! So now the mode can lie to us, without telling us on cot or anything.. And we are OK teaching it to do it for certain fields?? I wonder how is this implemented but the system card is packed with plenty of examples where the model lies and doesn't show the lie in cot...

If you are running a consulting business and you are deploying Anthropic or OpenAI directly into your organization (I’m looking at you PwC and Accenture) you are letting the fox into the hen house. OpenAI and Anthropic are openly funding and starting competitors to you while also using your usage to drive more success for them. This is not a failure on their part but a failure on your part. Consulting businesses that understand this are adopting a control plane that allows them to arbitrate where tokens go and who generates tokens for them. Controlling the tokens is controlling the spice (Dune). This was a key pillar of 8090’s global partnership with EY and they key feature of our Software Factory. We control token generation and can direct them to any model provider. We are close to another global partnership and will announce it soon. These organizations refuse to accept the disruption standing still or, even worse, by adopting and accelerating the companies who want to disrupt them.

just tried this out and it one-shotted* this video: "before the agent does anything" *i generated the narrative using chatgpt and used that as a prompt. featuring: @e2b @runanywhereai @composio @mem0ai @firecrawl @browser_use @agentmail @covenantlabsai some thoughts: - i clearly tried to stick too much into 30 seconds, they talk very fast and lost some content which breaks logic - character consistency is strong, i uploaded a single screenshot from my prior video as reference - voice consistency was not automatic. you notice unicorn switch from female to male voice part way through - the agent gives you an editor with generated scenes broken up but i don't see a way to regenerate a single section in the UI (which would be nice) - it is definitely a much better experience to have the agent stitch videos together than doing it yourself (i was using canva). was trying @flymy_ai's media agent api for it this weekend which also works well and with other models

Sigh…

This is baaaaad.

Ohh well here's a novel form of regulatory capture! Use your personal ChatGPT sub to get advice on a lawsuit? Unprivileged, other side can subpoena. Your lawyer uses their sub to ask the exact same questions, and forwards you the answers? Privileged, inadmissible in court!

it has been ruled that conversations with a major LLM providers are NOT considered privileged overtime, cases like this will increase demand for solutions like @covenantlabsai (encrypted LLMs) and @runanywhereai (run LLMs locally) that do NOT expose your AI chat data to entities who can turn it over to the government

Ads are coming to AI. But not to Claude. Keep thinking.

All the analysts forever writing about OpenAI vs Anthropic vs Google are missing the real story that already happened. 80% of startups pitching Andreessen Horowitz are running on Chinese open-source models. Not OpenAI. Not Anthropic. Chinese models like DeepSeek that cost 214x less per token. The math here breaks everything. DeepSeek trained its model for $5 million. OpenAI spent $500 million per six-month training cycle for GPT-5. That gap translates directly to API pricing where startups pay $0.14 per million tokens versus $30 for GPT-4. For a startup burning through 100 million tokens monthly, that’s $1,400 versus $300,000. The difference between 18 months of runway and 3 months. This tells you the real constraint in AI was never capability. Chinese models are matching GPT-4 on coding benchmarks while costing 2% as much. The constraint was always burn rate, and China solved it first by optimizing for efficiency instead of chasing AGI. The second-order effect gets interesting. When your infrastructure costs drop 98%, you can actually afford to fine-tune models for your specific use case. American startups paying OpenAI’s API rates are stuck with generic models. Chinese open-source users are building specialized variants. Silicon Valley thought the moat was model quality. Turns out the moat was cost structure, and they built it backwards. When a16z partner Anjney Midha says “it’s really China’s game right now” in open-source, he’s not talking about benchmarks. He’s talking about who controls the default foundation layer. Now look at where this goes. American AI labs are optimizing for AGI and superintelligence. Raising billions to chase the theoretical ceiling. China optimized for distribution and adoption. Making AI cheap enough to become infrastructure. All 16 top-ranked open-source models are Chinese. DeepSeek, Qwen, Yi. The models actually being deployed at scale. While OpenAI charges premium rates for exclusive access, Chinese labs are flooding the zone with free alternatives that work. The third-order cascade is what changes everything. Every startup that survives the next funding winter will have optimized around Chinese open-source as default infrastructure. Not as a China strategy. As a survival strategy. That 80% number at a16z only goes one direction. When you’re a seed-stage founder choosing between 18 months of runway or 3 months, economics beats nationalism every time. America is still competing to build the best model. China already won the race to build the one everyone uses.