For experimentation and token-minimizing I have been running some open-weight LLMs (e.g., llama3.2 for text and qwen3-coder:30b for code) locally on a MacBook via ollama within VS Code. I noticed that long-context tasks (e.g., analyzing a complex codebase and generating a new feature) can take a long time and compute, eventually heating up the device to a hazardous degree, especially under an ambient room temperature over 90°F.
https://www.instagram.com/p/DaXY3tDj8nM/
I created this little code repo as a fun exercise of LangGraph for a news agency that generates fictional news articles based on real events. Some of these articles are available on this Substack site.
I often find the patent disclosures hard to read (to the point that they could be good sleep inducers when one has a hard time falling asleep), and seem to be generated by some automatic procedures instead of directly written by humans even before the emergence of large language models (LLMs).
(If you have knowledge about this, feel free to let me know. I am pretty sure that in the minimum the patent law firms must have some sorts of document-templates even if not software.)
Fortunately, nowadays we can also use LLMs to summarize such documents (e.g., built-in within Acrobat Reader), which hints to a potential future in which all documents are synthesized and analyzed by AI, through which humans communicate with each other and with their AI agents, while the AI agents may evolve to communicate via their own languages that are more efficient than human languages but incomprehensible to humans.
25 April-1st fool’s days ago I joked that NVIDIA is buying Intel.
I think they should still do it, keep the design division and sell the manufacturing division.
(If only the antitrust regulators would allow it, which seems unlikely.)
The recent pay scales of some AI researchers have been astronomical in historical standards partly as a reflection of the exponential potential of AI on every aspect of the human society.
However, these attention-grabbing numbers apply only to a few people and there are nuances about claw-backs and performance expectations (similar to CEOs’ pay packages at similar scales).
No less interesting but less noticed is the fact that there are fewer headlines about non-financial aspects of AI researchers’ work, such as work-life balance, sense of purpose, and job satisfaction, which are often more important than the pay scale in the long run.
As we grow older, we may realize that time and life quality are more valuable than money and fame.
Get paid for doing what we love, instead of the other way around.
The UI of bsk.app, by none other than Jack Dorsey, looks exactly like Twitter.
Passive versus active (computer) vision.
Software versus hardware.
Assisted/driver-present versus autonomous/driver-less driving.
Passenger versus business target market.
Adobe Fresco is free for all now!
Give a try if you are into painting/drawing/motion and have a touch/stylus-supported device running iOS/Windows.
Compared to apps that have a larger audience (e.g., social media) or higher margins (e.g., business/productivity), digital painting aims for those who have the skills and interests to create art and who often do this for fun instead of work.
I guess this is why the Procreate CEO once commented that they make money in order to make the app, not the other way around.
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