19,366 karma · joined April 25, 2013
* Planning Scientist at Flight Science.ai
* Formerly: NASA JPL, Amazon Robotics, Outrider.ai, shield.ai
## What
Enjoys a little exercise, wishes for more time to build and read. Obsessive about embedded systems, planning algorithms, and applying optimization to far too many things.
## Web/Contact
* https://josh.vanderhook.info
* https://jodavaho.io
* https://mastodon.social/@jodavaho
* https://x.com/jodavaho
* meet.hn/city/44.9772995,-93.2654692/Minneapolis
## Keybase Verifier
[ my public key: https://keybase.io/jodavaho; my proof: https://keybase.io/jodavaho/sigs/LyGOcHPgNACB2NqAN9IU0Ps0Upm4nLekjyF12kgKNDo ]
jvanderbot.at.hn
Instead, focusing on policies that harm (or do not help) those factors you rightly point out sure does seem like a better way of handling it. Otherwise what, you have to choose between housing costs and mental health (when clearly housing costs contributes to mental health??).
Choosing to focus on everything is choosing to focus on nothing.
She said: “The evidence is clear that successive governments have failed to deliver on their promise to reduce suicide rates and make suicide prevention a priority. Today must act as a wake up call. The current approach is not working; we need meaningful action and proper funding if lives are to be saved.”
I understand the urge to do something on a large scale, but immediately pivoting to "the government" to solve personal mental health crises seems like a terrible idea.I was thinking just today about how, if I could hire my competition's lead designer/programmers, have them design a system for me cleanroom, that I'd effectively know everything there is to know about my competition's limitations. This is trivial to do with LLMs.
"Make me a thing that does X" is going to come out implemented basically the same way most of the time, as a kind of stack-overflow, well commented, internship project. (or, if undirected enough, as a collection of those based on a bog-standard architecture)
Same-y enough that you could train an adversary on it? Or hack it afterward? I am beginning to think so!
And suddenly we're looking at people who read ticker tape or by following with their finger for insights into neuroscience?
Of course, attention is almost certainly a heuristic readers use, but to even bring it up as key to intelligence begs so many questions about LLMs and the mind...
For some reason this is controversial, but it remains true.
It takes me generally 3 months to find a good job, and while the industry is changing I suspect you'll be ok. Nobody I know is hunting for jobs.
Hang in there
Combine this with a growing sense of being "left behind" and a highly publicized collapse in software jobs coming any day now and any proof of contribution is highly prized. So why not?
1) Privacy/ ad-protection native (as you say)
2) High security and performance (fully tuned DD-WRT). Even solving bufferbloat for end users would be amazing - the world is barely catching up on that.
3) Good software add-ons that aren't proprietary junk. Asset management/upgrade, sign-in/landing pages, monitoring and trafffic shaping that holds up, etc.
Bunlding FOSS to solve these problems well, even on some else's hardware, does seem like work, and work can be charged for.
Just calling the progression while we're on it.
One would take on an intellectual journey of discovery, sailing off into the pacific to learn to live off grid, writing colorful narrative-driven articles, full of artwork, mission statements, and subtle suggestions of donations. They would emphasize the human nature of discovery and seek to engage you as they went. Come to the enlightened tech path! Some neat new technology would be built along the way around these principles, mostly for their own use.
https://100r.co/site/projects.html
But the other group would write a self-hosting Forth targeting the top 10 most used CPU platforms of all time and post a text-only HTML site with download links. It would work everywhere. You’d know nothing about them and they’d probably run linux on a 17 year old mac.
Personally, I’m squarely in the second camp, but I wish dearly I had the creativity and spine to be in the first camp.
You can also read this as diminishing returns / AI isn't good enough, etc, but the simplest explanation is that they don't want to send money to Anthropic.
Tunnels and basements just trap it so it can't blow away.
Both use the softmax operation, but "probability of emitting a given label" is not the same as "probability the decided action is correct". OTOH, taking an action to sample something to come up with "probability of emitting a label" is in fact the whole shebang if the label is the next action. If it works it works.
Who cares, this distinction is small.
For me, a planning guy, either your suggestion or Jev-like would work just fine, and you better believe I'll be testing both as I'm quite excited about being able to use less-structured, untrained systems in decision pipelines. Thanks for the heads up.
You're asking me how a structured output which assigns probabilities to classifications of the input text is different than the next-token weights which are calculated while generating that output?
One is internal (token prob), one is output generated by that internal mechanism e.g.,
"{ next_state: { "evade": 0.84, "land": 0.10, "search": 0.01"...}}"Find me a another class of 0.4 B model that can handle structured output decision problems with the same latency and accuracy as Qwen 3.6.
The benefits of Jev, and the reason I'm excited about them:
* Pre-trained/tuned to provide only structured output
* Small, fast, essentially trivial to locally run - this alone makes them an actual candidate for real-world planning systems. Laya is a local-first 400 million parameter model, for example.
* Their confidence scores are useful, even if those scores are not correct/true probabilities.
For me and my work, they look like nearly turn-key, tiny, fast, locally-hostable models that can manage state transitions in a deeply autonomous system.
LLM-as judge requires, well, what we know as a full LLM. A fully trained classifier model requires fully training - something you cannot / won't do for a embedded autonomous planning system (by contradiction - if this worked, we'd have used it everywhere already!). Totally unfair comparison in my mind.
I have an excerpt of the book explaining this here: https://jodavaho.io/posts/books-blindsight.html
The discussion about non-conscious intelligence was less so (I see ants every day!). But this is a new idea dense book to say the least - if you love worldbuilding, please pick it up.
Jumping from "money making via tax breaks" to "murder" is quite a jump, also. These are along a spectrum of choices.