WATCH: @tequilamjohnson of @EquityAlliance1 asks why @RepCohen is sitting on a $1.8 Million war chest after he retired from Congress instead of helping Memphis Dems who are running

Recommending the "drink big water" theory. Ever since I started drinking big water, my coffee cravings have dropped exponentially. After a highly insufficient evaluation, my pick: Nongfu Spring, the 1.5L one.

Recommendation: Venice AI One of the biggest frustrations with mainstream AI services is how often they refuse perfectly legitimate requests. Models from providers like OpenAI and Anthropic have extensive safety restrictions that can get in the way of cybersecurity research, biotechnology discussions, creative writing, or even harmless roleplay. Sometimes you just want an AI that answers your questions without constantly telling you what you can and cannot do. Of course, you can run uncensored open-weight models locally, but not everyone has the hardware or technical knowledge for that. This is where Venice AI (@AskVenice) comes in. It gives you access to a huge selection of AI models, including uncensored and "abliterated" variants with many of the usual safety restrictions removed. No expensive GPU or complicated setup required. And Venice goes far beyond text chat. You can generate and edit images, create videos from text or images, generate music and audio, build custom AI characters for roleplay, write code, analyze documents, and perform research. There's also an API for developers who want to integrate these models into their own applications or AI agents. Another major selling point is privacy, with private models that don't log your conversations. The appeal is having much more freedom to explore and experiment with AI, including subjects and creative ideas that mainstream services might reject. There are still legal boundaries and some individual models retain their own restrictions, but overall it's a very different experience. One final note: I'm not affiliated with or sponsored by Venice AI in any way. I've simply been testing the service as a paying customer and thought it was worth sharing.

Recommended reading for the weekend: 1. @jpmorgan — Credit markets look for AI’s weak links 2. @BernsteinPWM — Climbing the Wall of Worry | 3Q Macro & Markets Insights 3. @gdb — Towards securing the internet 4. @AswathDamodaran — From Eyeballs to ARR: Buzzy Pricing Metrics and the AI Trade! 5. @UBS — Higher oil and AI concerns drive risk-off move 6. @neil_shah — NVIDIA GTC Berlin 2026 – Containers, Safety and Sovereignty Rough week in the markets. Hope this weekend serves as some welcome respite!

Recommended reading for the weekend: 1. @FundaAI — Physical AI: From the Robot-Data Race to a Second Demand Curve for Compute 2. @Citadel — October Update: Fundamentals in Focus 3. @satyanadella — Models as Insider Risks in the Super Intelligence Era 4. @KobeissiLetter — The "Stealth" Bear Market: A Historic Opportunity 5. @arthurmensch — Build or surrender 6. @Cayden_Liao — The Biggest Hack in Crypto History That Never Happened Have a great weekend!

Recommend a movie that destroys you in the most beautiful way. A deeply human story about love, loss, loneliness, regret or the complicated mess of being alive. Not just something sad, but a film that makes you feel connected to its characters and leaves you sitting in silence long after the credits roll. The kind of movie that stays with you for days.

Recommended paper from Sakana AI on recursive self-improvement. They propose an interesting way to scale recursive self-improvement through multi-agent self-supervision. In this line of research, self-improvement loops usually need an external verifier, so open-ended tasks without a checker are left out. MASS removes that requirement. One base model proposes multi-agent workflows, runs them and grades them, and an evolutionary search keeps the workflows that score best. The model is then fine-tuned on its own traces, and the improved model starts the next cycle as a better optimizer and grader. Two cycles on Qwen3.6-27B raise performance per output token from 1.2 to 1.6x on four open-ended benchmarks. A student trained on multi-agent traces also beats a single-agent student trained on 1.4x more tokens. Paper: Chat with Paper:

Recommended reading for the weekend: 1. @AswathDamodaran — Earnings, Cashflows and Stock Prices: The AI Spending plays through! 2. @mustafasuleyman — AI won't take your job anytime soon. 3. @BNPParibas — Investment Strategy Focus 4. @EpochAIResearch — AI in 2030 5. @RANDCorporation — Mapping AI Ecosystems in the Netherlands and the United Kingdom Have a great weekend!