It's my understanding that when you purchase content you get a license that is limited to terms the seller defines (i.e. only if the content is delivered from seller's approved data sources, only if the content is viewed on a seller approved device, only if the content has a seller approved drm, etc).
I can't find any references to the company online anymore. The product was a c++ interpreter (created from the founder's compiler class project) and IDE. This was before java/python/javascript/etc. The interpreter would catch errors and you could mix interpreted code and binary code. Pure software displaced it.
In the late 80s, a friend turned his comsci class project into a product.
The company was making about $25m. It died due to a patent dispute.
He later started a dot com that is still very successful.
I hope we create whalegemma (similar to dolphingemma) so we can explain to them how to co-exist better with humans (e.g. avoid this area during their whale hunting season, travel to this area if you get sick or tangled in rope).
I've noticed 2 very obvious trends. The high-performers who previously wrote the most PRs are now using AI the most aggressively and the number PRs they produce has almost doubled. The other trend is that large teams are being split into smaller teams that work on new products. I hope the trend is more projects and less grunt coding. (I know the ai coders are not yet L6 but I suspect they will achieve L3-L5 this year).
I suspect we need to build MCP servers that prevent destructive commands.
For example, we need a "bash" tool doesn't invoke /usr/bin executables directly.
The agent should think it is invoking a unix command but those commands are proxies that prevent destructive operations with no ability for an agent to circumvent the restrictions. If there isn't a MCP server for your specific setup/need, building one just for your need should be your first step.
I use 2 32" 4K which cost about $800 for both monitors. The small gap between the monitors is annoying but I can't really justify paying $2k more. Also there is a samsung dual 4k that is about the same price as the dell.
Moving my head to see everything doesn't bother me. I also have a setup with 3 32" 4k which I find a little too wide but in that setup 1 monitor connects to different computer.
The AI market is hard to predict due to the constant development of new algorithms that could emerge unexpectedly. Refer to this summary of Ilya's opinions for insights into the necessity of these new algorithms: https://youtu.be/DcrXHTOxi3I
DeepSeek is a valuable product, but its open-source nature makes it difficult to displace larger competitors. Any advancements can be quickly adopted, and in fact, it may inadvertently strengthen these companies by highlighting weaknesses in their current strategies.
Basically, when network connectivity increases, the "bad" nodes can overwhelm the "good" nodes. The other ideas discussed are really interesting; well worth watching.
I think those of us who want youtube without the distractions should start a campaign to convince google to make https://www.youtube.com/learning better. I think even tweeting about it so that our efforts are reported by large news outlets would work. If there aren't enough users asking for this feature, we will be ignored. Someone needs to build a website that coordinates our efforts.
Preventing garbage just requires that you take into account the cognitive limits of the agent. For example ...
1) Don't ask for large / complex change. Ask for a plan but ask it to implement the plan in small steps and ask the model to test each step before starting the next.
2) For really complex steps, ask the model to write code to visualize the problem and solution.
3) If the model fails on a given step, ask it to add logging to the code, save the logs, run the tests and the review the logs to determine what went wrong. Do this repeatedly until the step works well.
4) Ask the model to look at your existing code and determine how it was designed to implement a task. Some times the model will put all of the changes in one file but your code has a cleaner design the model doesn't take into account.
I've seen other people blog about their tricks and tips. I do still see garbage results but not as high as 95%.
What do you think of the recent anthropic research on how LLMs reason? It is clear from their shallow analysis that LLMs have very serious reasoning weaknesses. I wonder if those weaknesses can be "addressed" if we build LLMs that can do deeper analysis and use RL to self-improve. LLMs improving LLMs would be a very impressive step towards AGI.