--- # an impending paradigm shift in communication technological revolutions are often accompanied -- and sometimes caused -- by a shift in communication paradigms. language, writing, printing, telegraph, radio, television, email, the web, social networks, and instant messaging. they're revolutionary in different ways, but they have two thing in common: they were invented and built to be used by humans -- and accelerated the speed of communication by order of magnitude ... ... what is missing, is the idea of how this shift is going to go and what it's going to mean. think of megatrends of ai -- whatever happens, they happen. whoever is leading the ai race in 2050, these are invariants. while the implementation details & dates will vary wildly, i believe that these megatrends will come to fruition, and the early bird will catch a big billion dollar worm. **post**: [[an impending paradigm shift in communication]] **date**: 22 oct 2025 --- # how do you know your followers are humans? ![[poster-005-followers-are-humans.jpg]] i bet $10 bucks that a bunch of your followers are bots -- and this erodes the foundation of our ability for economic transactions indeed, how do you know your followers are humans? **post**: [[poster-005-followers-are-humans]] **date**: 14 oct 2025 --- # punk rock moment of ai is close and it will reshape how we use this technology ![[poster-006-punk-rock-moment.png]] societal change is in the air and everyone feels it, but you gotta pave the way. like punk rock soared in the eighties -- because everybody was fed up with glam rock. but it took a musical genius to understand that and propose an alternative. **post**: [[poster-006-punk-rock-moment]] **date**: 21 sept 2025 --- # ordering food is frustrating -- and not very promising for agentic automation ![[poster-004-ordering-burgers.jpg]] to order a single burger at mcdonald's i had to do 21 freaking clicks then one more action to pay, one more to pick up the check, and one more to get the table number. 24 actions in total. --> read more **post**: [[poster-004-ordering-burgers]] **date**: 14 sept 2025 --- # last mile automation - human glue between hundreds of automation tools ![[banana bruegel delivery banner.png]] - automation tools don't work because they never provide complete, smooth, end-to-end automation - in business and private life, we spend hours to avoid them, weeks get used to them, and months to fix - companies also tend to throw in tens of thousands for customizations, which in the long-term make things worse - in logistics, the problem of last mile delivery is still solved by meat robots ie your delivery driver, even though drones and robots are coming for it - in software, the problem of last mile automation is about automating those clicks, file saves, email sends, integrations that still need a monkey to smash their fingers on a keyboard **post**: [[last mile automation]] **date**: 2 sept 2024 // july 2025 --- # screen navigation is a precursor of total automation ![[Pasted image 20250831012500.png]] **summary**: - screen navigation emerged rapidly after gpt-4 vision release with early examples like vimgpt and tarsier in november 2023 - a wave of products followed in late 2024/early 2025 including anthropic computer use, google project mariner, and openai operator - screen navigation provides universal integration by making human interfaces accessible to ai without requiring apis - the article argues this approach is fundamentally flawed as it automates existing inefficient processes - the author predicts screen navigation will become less important as interfaces evolve to be natively designed for ai interaction - screen navigation is merely a capability of language models, not what makes them agentic - the future lies in "ai-first systems" built by ai for ai that eliminate human inefficiencies - the transition to machine-to-machine communication will progress from screen navigation to app-to-app integration to organization-level ai communication **post**: [[screen navigation is a precursor of total automation]] **date**: 31 aug 2025 --- # and for how many years have you not used intellij idea and pycharm ![[Pasted image 20250801222154.png]] that's what i got and you? **post**: [[and for how many years have you not used intellij idea and pycharm]] **date**: 1 aug 2025 --- # consumer electronics device for ai ![[ai device.png]] **summary**: - the post questions whether users truly own and control the ai they use, as cloud-based ai is aligned with companies, not users - ai model costs are decreasing while quality increases, suggesting consumer-grade laptops will soon run powerful models locally - running numerous ai agents (10,000+) on personal laptops isn't feasible due to hardware limitations and shared access needs - cloud-based alternatives would cost $10-15k monthly now, potentially dropping to $100-300 monthly in two years - the author proposes a dedicated consumer ai computing device ($100/month lease) that could serve multiple users and sell unused capacity - ai is becoming a utility like electricity - decentralized, widely available and locally accessible - economics will eventually favor local ai infrastructure at community levels rather than dependence on a few global companies **post**: [[consumer electronics device for ai]] **date**: 20 july 2025 --- # expect ai magic to continue **summary**: - as assistants emerged from the base models through few-shot prompting for ai-2-user style responses to instruction tuning to rlhf, now reasoning capabilities are emerging from base via chain of thought prompting & traces to cot instruct and rlvr - the magic of ai is similar to traditional ideas of magic - saying words to make things happen without physical intervention - in-context learning (icl) is a groundbreaking capability that emerged unexpectedly from transformer models, allowing customized behaviors at a fraction of the cost of training or fine-tuning - these emergent phenomena represent "ai magic" that will continue with new capabilities beyond reasoning (eg, tool & computer use, vision, multimodality, ... n) - i suggest experimenting with icl to generate traces, fine-tuning models with reinforcement learning for specialized capabilities, and creating unique model collections **post**: [[expect ai magic to continue]] **date**: 9 may 2025 --- # google can do whatever the fuck they want with your licenses **summary**: - google is arbitrarily increasing prices, forcing gemini features on users without their consent - changing email, sso, or cloud providers is such a massive undertaking that most won't do it over a 5% price increase - big tech companies have walled off the internet and profit from it, making the author desire significant change **post**: [[google can do whatever the fuck they want with your licenses]] **date**: 1 may 2025 --- # palantir, tesla, morgan stanley are evil and there is no way around it **summary**: - palantir is about mass surveillance and population control, not freedom and protection - tesla has betrayed its mission with elon musk becoming a psychopathic oligarch who cares only about personal wealth - morgan stanley is responsible for the 2008/9 crisis, creates toxic work environments, and financializes things like housing and education that shouldn't be commodified - employees at these companies are morally complicit and should be held accountable - our collective tolerance of these organizations reveals a disturbing ethical landscape in 2025 **post**: [[palantir, tesla, morgan stanley are evil and there is no way around it]] **date**: 22 apr 2025 --- # reverse engineering commit history to train reasoning models on process rewards **summary**: - current language models are trained on static code repositories but miss the evolution process (requirements, implementations, changes) - commit history contains valuable information about how code evolves over time - proposal: reverse engineer commit history to generate prompts that would produce each commit's code changes - create a dataset of repository states interleaved with these generated prompts - use this to train reasoning models with reinforcement learning using "process rewards" - verify each step by comparing model-generated changes against actual commits **post**: [[reverse engineering commit history to train reasoning models on process rewards]] **date**: 21 apr 2025 --- # non-destructive revolution **summary**: - unlike communist revolutions that destroy existing systems, we can radically change systems without ruining them - system analysis gives us tools to understand complex systems, their components, and how rules impact outcomes - organization is a human strength that helped us dominate the planet, and most problems stem from poor organization "don't blame the player blame the game" - when the right ideas take hold, believers can gather and demand change **post**: [[non-destructive revolution]] **date**: 18 apr 2025 --- # ai adoption gap **summary**: - despite widespread ai awareness, actual adoption rates remain surprisingly low - workshops with 300 participants across 100 german companies revealed many professionals haven't used tools like chatgpt at all - while reportedly 13.5% of german enterprises have some ai knowledge, true power users likely represent only about 1% - the slow penetration rate is unexpected given the technology's groundbreaking nature and general awareness **post**: [[ai adoption gap]] **date**: 17 apr 2025 --- # ai agents transforming enterprises ![[cover.png]] **summary**: - ai has evolved from its classical definition as computer science simulation of human intelligence to include behavior-based definitions and advanced cognitive capabilities - modern ai systems can be categorized as: 1. assistants (dialogue-oriented user support) 2. reasoning models (logical thinking and problem-solving) 3. agents (autonomous systems that can act independently with tools) - organizations are implementing ai for: - developing company-specific knowledge bases - process automation for productivity gains - specific use cases include: - automated order processing (reducing manual data entry) - patent analysis (reducing analysis time from 4 hours to 15 minutes) - resume evaluation (providing detailed assessment beyond 6-second human scans) - major pitfalls of ai include: - technological challenges: hallucinations, context recall problems, reliability issues - economic/social concerns: job displacement, market concentration, intellectual property issues - the ai market is increasingly concentrated among a few companies with frontier-level models, raising concerns about power distribution **post**: [[keynote - ai agents transforming enterprises]] **date**: 15 apr 2025 --- # the difference between a dead man and a billionaire ![[Pasted image 20250415160641.png]] **summary**: - aaron swartz faced 35 years in prison and committed suicide at 26 for sharing scientific papers, while zuckerberg's company used pirated books for ai training with minimal consequences - the author questions the fairness of intellectual property systems that punish individuals like swartz while allowing tech giants to exploit similar practices - the piece criticizes figures like musk and dorsey who advocate abolishing IP rights, suggesting this would primarily benefit big tech companies **post**: [[the difference between a dead man and a billionaire]] **date**: 15 apr 2025 --- # alignment through structure ![[Pasted image 20250502125940.png]] **summary**: - ai alignment cannot be achieved through goodwill, reward functions, governance, or technical methods alone - humans and organizations will inevitably exploit any possible misuse of ai for power and wealth - key structural requirements for ai alignment: 1. distributed ownership of data, models, and compute 2. fair distribution of resources, capabilities, and usage 3. decentralized control, governance, and power - tech giants are currently shaping ai to serve their interests rather than society's - we have until 2030 to establish the structural foundations for aligned ai **post**: [[alignment through structure]] **date**: 13 apr 2025 --- # teaching ai to navigate screens **summary**: - ai agents struggle with screen navigation because they operate at the wrong level of abstraction - focusing on low-level clicking rather than interface patterns - the solution is creating an "imprint" system with two apis: "lm-in" (for language models to request actions) and "ui-out" (pre-learned interface navigation) - screen recording tools could capture human workflows, allowing ai to generate scripts that automate repetitive tasks - training on tutorial videos and simulated environments would enable more reliable, verifiable screen navigation - this approach would make ai navigation faster, more precise, and personalized to users' actual workflows **post**: [[teaching ai to navigate screens]] **date**: 12 apr 2025 --- # ai make ai **summary**: - agent frameworks are finally showing promise after years of failures, with tools like bolt.new and cursor building functional apps - recent research (meta agent search and ai scientist) demonstrates ai can create and improve other ai systems autonomously - we're approaching a point where ai creating ai will surpass human capabilities, potentially leading to a "kembrian explosion" of ai agents that we can create in a matter of minutes **post**: [[ai make ai]] **date**: 31 mar 2025 --- # os-complete navigation environment **summary**: - navigation/screen navigation is about language models looking at screens, understanding elements, and clicking to complete tasks - current issues: imprecise, slow due to continuous screenshots, vision processing, parsing, and command generation - proposed solution: "imprint api" - javascript-based api that translates lm commands into actual clicks - imprint api is generated by a screen reader and vision model pre-trained on interfaces, creating latent representations where similar functions across different uis map to similar endpoints - "portables" (html simulations of interfaces) can create training environments without using actual websites - an "os-complete navigation environment" would contain portables, screen reader models, and interaction traces to train language models - such environments will be essential for training models to effectively use computer systems **post**: [[os-complete navigation environment]] **date**: 10 mar 2025 --- # cafe still life with diffusion models **summary**: - reasoning models like sonnet37r or gemini20 flash fast thinking are great at extracting detailed descriptions of images - metaprompting allows for generation of consistent images quite similar to the original **post**: [[cafe still life with diffusion models]] **date**: 10 mar 2025 ![[_media/IMG_20250309_153048.jpg]] --- # cost of intelligence will drop 100.000x within 24 months **summary**: - cost of intelligence dropped over 10x within 18 months, as measured by the price of openai gpt api - in the next 18-24 months, it can drop by 100x due to model optimization, and another 100x due to hardware. those are orthogonal and can be multiplied - what will happen to the demand for ai? **post**: [[cost of intelligence]] **date**: 8 mar 2025 --- --start--