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Jensen taught me a lot. But the most important things he taught me was to care about the details. And keep pushing no matter how long it takes you. 3 years ago, he invited me to fly on his jet with him. I was shocked. And he did it because he knew I was this unrelenting person that chased Snowflake for years to make the impossible a reality. He knew I was that person and he remembered I was that person. Few people at his level care to do either. He did both. I owe him a lot. He built a great company that changed my life forever. And he gave me the means to execute on my ambitions in a way that nothing else ever could. Praying for him, his family and everyone at NVIDIA past and present. PS. Photo is not AI generated. This flight was to Snowflake's conference!

A beautiful reminder that nature’s greatest stories are often told without words. 🐦❤️ A mother’s love, a tiny life, and a bond that makes every moment magical. Follow @Aistzone for more extraordinary AI-generated bird stories #AIWildlife# #BirdLovers# #NatureBeauty# #Aistzone#

I've been doing SEO for 10 years and I've just spent 2 weeks obsessing over the design of our new SaaS blog. I think I've finally cracked it. The perfect blog design for SEO, AI search and actually turning readers into customers. (I'm rolling it out on soon and I'll share the results) Here's how you can do the same: 1. Optimise the title and metadata Use a keyword-focused H1 that matches search intent. Make the topic clear and include accurate publication and update dates. 2. Establish credibility immediately Show who wrote and reviewed the article, their credentials and first-hand experience. Give readers a reason to trust it. 3. Present your solution above the fold If someone lands with a problem your product solves, why make them scroll to discover it? Show your solution immediately with a relevant offer and CTA. 4. Lead with key takeaways Summarise the most important information at the top. Use short, scannable bullet points that help readers find answers quickly and AI systems interpret your content. 5. Keep your CTA visible Add a sticky sidebar as readers scroll. Highlight your product's key benefits and make it easy to get started. 6. Structure content for SEO and AI search Use descriptive H2s and H3s, short paragraphs, bullets and internal links. Answer questions directly so readers and search engines can follow. 7. Use tables and original visuals Include comparison tables, original screenshots, diagrams and real examples. Give readers something useful they won't find in generic AI-generated articles. 8. Add contextual conversion opportunities Don't rely on one CTA. Introduce your product naturally where it's relevant, connecting each offer to the problem you're solving. 9. Back up your claims with evidence Use original research, real data, expert insights and credible sources. Don't just tell people something works, show them the evidence. 10. Finish with a strong conversion CTA Once someone's finished reading, give them a next step. Reinforce your product's benefits, add social proof and make it easy to convert. A few extra things: Add a table of contents for navigation, FAQs for related questions and related articles to keep readers exploring through internal links. And most importantly, measure the results. Not just rankings, traffic and AI citations, but actual signups, leads and revenue. There's no point getting 1,000s of visitors to your blog if none of them become customers. And there's no point having a beautiful, high-converting blog if nobody can find it. The goal is to get discovered AND convert. I've included the full visual cheatsheet below: (Feel free to steal the design for your own site)

Today, we're expanding SynthID Detector in partnership with @OpenAI, @NVIDIA, Kakao & soon @Apple as part of an industry-wide effort to make AI-generated content more transparent. Available globally in English, our verification portal lets you check if media was made with AI 🧵

A man’s 10.5-year prison sentence for manslaughter was tossed out after AI was used at his sentencing hearing. The family of Christopher Pelkey gave several victim impact statements in court — but the one that stood out was a statement from Pelkey himself, even though he died in a roadrage incident in 2021. Pelkey’s sister wrote what she believed he would have said if he were alive and had an AI-generated version of him read it in court. But lawyers for Gabriel Paul Horcasitas, the man convicted in Pelkey’s killing, appealed his sentencing because of this AI video — and they won.

In my interaction with the GSMA Board, stressed the following: Technology for the last mile. Affordable access for the Global South. DPI as a force multiplier. Technology-led solutions to cyber fraud. Global standards for emerging tech. Clear identification of AI-generated and deepfake content. Our digital future must expand opportunity while strengthening trust.

Afro House 2026 Nomad Soul I - Echoes of Kilimanjaro (Tribal Afro House) 🎶 Afro House Vibes meets AI Innovation 🎶 "Nomad Soul I - Echoes of Kilimanjaro" – An uplifting Afro House journey by K - AI Music. 🎶 Welcome to the World of AI Music! 🎶 Dive into the warm and hypnotic world of Nomad Soul I - Echoes of Kilimanjaro, an AI-generated Afro House track blending tribal percussion, organic rhythms, deep grooves, atmospheric melodies, and immersive sunset energy. 🌍 **Nomad Soul I - Echoes of Kilimanjaro** is an immersive AI-assisted Afro House / Organic House journey inspired by the majestic landscapes of Africa, the spirit of nomadic cultures, distant mountain horizons, and the timeless energy of the wild. Deep Afro grooves, organic percussion, hypnotic basslines, warm atmospheric synths, emotional melodies, and cinematic soundscapes come together to create a powerful yet peaceful electronic experience where earthy rhythms meet modern production. 🏔️🌅✨ The track unfolds through rolling tribal-inspired drums, organic textures, subtle vocal elements, deep low-end movement, and evolving melodic layers that build a natural connection between rhythm, atmosphere, and emotion. From the vast African plains and golden savannahs to the towering presence of Mount Kilimanjaro beneath an endless sky, **Nomad Soul I - Echoes of Kilimanjaro** captures the feeling of wandering freely through ancient landscapes, combining the hypnotic pulse of Afro House with the spacious, soulful character of Organic House. Every layer moves like a journey through changing horizons, balancing deep energy, warmth, serenity, and cinematic depth. 🌍🥁🏔️ Perfect for **Afro House playlists, Organic House mixes, sunset sessions, beach and rooftop vibes, travel videos, African-inspired visuals, chillout playlists, lounge environments, summer workouts, meditation, relaxation, yoga sessions, and late-night DJ sets**, **Nomad Soul I - Echoes of Kilimanjaro** invites listeners into a rhythmic journey where ancient-inspired grooves meet contemporary electronic sound. Let the deep percussion, warm harmonies, hypnotic basslines, and atmospheric melodies carry you across open landscapes and distant horizons, following the echoes of Kilimanjaro into a world of freedom, movement, and timeless soul. 🎧🌅🏔️ Perfect for: ☀️ Sunset Sessions 🏝️ Beach Vibes 🌍 Travel Videos 🎶 House Music Playlists 🔥 Festival Atmospheres 🌅 Golden Hour Moments ⚠️ All content is original. Vocals are AI-generated and not intended to imitate any real artist. Any resemblance is coincidental. 🧠 This is emotional storytelling through AI – not just sound, but sensation. #AfroHouse# #OrganicHouse# #TribalHouse# #HouseMusic# #AIMusic# #ElectronicMusic# #KAIMusicACK# #DeepHouse# #shorts#

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis: