PPS: Here's another before/after illustration for how we may think about AI, AGI and it's impacts (Originally from Rem Kolhaas' Delirious New York) Before 1930: What we think this is After 1930: What the future actually is and means

I wholeheartedly agree with Tom Bedor's thesis: "If You are Asking for Human Attention, Demonstrate Human Effort" over at tombedor.dev/human-attention…

Yesterday I organised Europe’s first community Cafe Compute with @cerebras in Berlin! We had over 200 signups and ~80 people join us to talk about fast inference and running frontier intelligence at the speed of 1000 tokens per second. Special thank you for Cerebras and @communidiyi for entrusting me with the Ambassadorship and @WigetJanette with @aicampusberlin for hosting us and helping me organise this evening. In the first half we had Zigfrid (Solutions Architect at Cerebras) join us and answer all the questions we had about what they do and how their chips work. And in the second half we had great demos from the community: @slobkebap presented a real time GenUI flow to build UI in realtime by talking to it using @OpenAIDevs's realtime v2 api combined with Cerebras GLM-4.7 @SpringStreetNYC presented his autonomous AI research lab experiment in which he tried to teach a small model to ask great questions on the level of frontier models. Hoang Minh showcased how he finds vulnerabilities in big OSS projects very fast and helps patch them. And my personal favourite demo that night: @leolurch, who showed us his machine learning pipeline to find lost art pieces on auction platforms. Basically face recognition but for art, to recover potentially stolen or simply lost art from the last centuries. I had so many cool chats and am glad that so many very talented people showed up. Thank you everyone for coming! And thank you so much @0xSero for sharing the event!

Me, trying to touch the future while giving a quick demo of "'Which city in Paris are you staying in?'–Can an LLM autoresearch loop teach a phone-sized small language model (SLM) to ask frontier-quality questions? Can it? Does it? Let's find out!"

Hey @LumaHQ! You app is absolutely fantastic. In only three weeks, I've been to five top-notch events meeting new, totally cool people. But now that you've solved meeting new people for (thank you), I have a NEW problem: As the events start to wrap up, we often want to stay in touch. Cool! Yay! Now, to "connect-connect", the norm is to use LinkedIn. So, now I've to go back to (*shudder*) LinkedIn as well. You know, that place. And it's super-duper awkward: their app has a QR-code contact scan thing that gets more embarrassing every passing second while you're both fiddling with your phone ("...oh, doesn't work, there's a glare on your screen. Oh, it's my camera?...") After yesterday's event (awesome btw.), I seriously consider printing contact cards. But then, what do I put on there? My phone? My email? My Twitter? And then? What actually HAS worked a few times: I meet them, memorize their first name, then go to Luma, check the participant list (when it's visible) for their first name, when there are multiple "Davids", deduce based on first impressions which one and then try to find them online. Somehow. So, team Luma, as a huge fan: I would like to ask you to let me find and connect and stay in touch with these new peers via your app. Thank you so much :)

PS: For anyone interested, a more detailed project onboarding is here: alexisrondeau.me/tada/resear…

But basically I'm using frontier models to train a local ("homestead"?) model as to impart some of that good brain stuff but minus the overwhelm. Like a teacher, hopefully.

LOL, yeah, this one also not a keeper. An old friend of mine shared this nugget of wisdom from art school with me: > "Good from far, but far from good."

Okay! Adding a domain-specific LoRA adapter moved the needle the most. I bootstrapped an autoresearch loop that initially went through a bunch of experiments that tested a bunch of prompt/sequence/selection/critique and reflexion combos. Those didn't really result in much lift. PS: If you haven't seen this yet, Apple provides the "Foundation Models Adapter Training Toolkit" over at developer.apple.com/apple-in…

Infermation (/ɪnfərˈmeɪʃən/; portmanteau of inference and information) is knowledge produced by inferential processes rather than direct observation, measurement, or first-hand transmission. Unlike conventional information, which is grounded in a source, infermation is derived — synthesized from patterns, priors, and partial evidence.

When I say I'm in love You best believe I'm in love, L-U-V