sieve
Joined 5 years ago
Joined 5 years ago
Both bcachefs and ZFS are social problems, not technical. Nothing stops a distro from shipping ZFS as part of their kernel. Except a legal interpretation of whether CDDL and GPL2.0 allow that marriage.
We have no idea when the Rāmāyaṇa was composed or who composed it. We traditionally attribute it to Vālmīki and call him the ādikavi and the work the ādikāvyam -- first poet and first piece of poetry respectively. The entire set up presents it as the story of a man named Rāma.
The poet specifically says in the fifth sarga of the Bālakāṇḍa:
तदिदं वर्तयिष्यामि सर्वं निखिलमादितः।
धर्मकामार्थसहितं श्रोतव्यमनसूयया॥ ४
"Now I shall recite the entire thing from start to end. As it relates to dharma, kāma and artha, it should be heard without complaint/malice/envy/etc."
The neo-Western standard of written or epigraphic evidence being the only real evidence was not really that relevant in the ancient world. Hinduism itself is based on the concept of time as a cycle instead of the linearity that is common in Western religions.
So, the story could have received its present form some time in the past 3-4K years, and has been fiddled with by grammar-obsessed scribes across the country over the past 2K years, but it could encode memories from a time long before that. The story has many fantastical elements like the tale of Sagara and his 60K sons and the descent of the river Gaṅgā from the heavens, but it is much more reserved in other aspects. The etymology of the term vānara itself is debatable. Has it been derived from narō vā (is it a man/human)? Or vanam + naraḥ (forest + man = forest dweller)?
I have a 5 kWp solar plant that produces ~25kWh per day, but it is grid-tied. I considered a hybrid setup with LFP battery back up, but decided to separate the two systems to avoid a single point of failure.
This has been my hobby for nearly two decades and it is not so easy because there are hundreds of edge cases that must be considered even if the LLM writes all the code.
He does not understand that it is just like a phone. Unless you keep it on standby (which he does not want to do: wastes power), it will boot, slowly, till all the subsystems are up and running. The low powered processors, the low speed ssds, and minimal RAM makes everything worse.
- You pay $10/mo for Go and get $10 worth of usage per month
- Now, each model they make available has a multiplier from 1.5-8x
- So, the expensive ones like Kimi K3 have a 1.5x multiplier, cheaper ones like DeepSeek 4.1 Flash have 8x
- This gives you usage of $15 and $80 per month respectively
- You can mix and match
- So, you can use 25% of your quota with K3 and 75% with DS41Flash ... usage of $3.75 and $60 respectively
- You divide the two figures by their respective multiplier ... $3.75 / 1.5 and $60 / 8 ... and you end up with $2.5 + $7.5 = $10 which is what you paid
- You should ignore the requests table and go directly to the price/M tokens table where all the actual rates are given
- They control usage at 5h/1w/1mo intervals
- You can use 25% of your monthly quota in a 5h block, 50% in a week, and 100% in a month.
- If you were only using DS41Flash, that is $20m, $40 and $80 worth of usage respectively which is $2.5, $5 and $10 of your subscription figure after accounting for the multipliers.
It would be so much simpler if they credited 1M microdollars per $1 of subscription and quoted the per M token rates for each model in this currency.
But then Claude/Codex/StepFun and all the other subscription services are far more opaque.
- irc.example.org
- news.example.org
could be the same thing. IRC would poll. NEWS would not.
- You can change the back end and store everything in an sqlite3 database.
- Spam is what killed usenet as a discussion medium. Topic-specific, moderated google groups are still active and see traffic.
- People keep reinventing the same things over and over again.
The goal is to get the 31B working on a 16GB card. Which means weights have to be < 4 bits. The 9070 XT has wmma support for int4. Which doubles compute performance on pp compared to i8/fp8. tg is limited by bandwidth.
Only once? This has happened to me so many times with LLM-assisted development. I was writing an OCR engine and pathological shapes would cause OOM that would take the display out. On other occasions, the LLM would try to load 24GB of bf16 weights onto a 16GB GPU without understanding the consequences.
Have to encode this in logic: keep some space reserved for the display, and check before allocation. But some errors are bound to slip through.
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Inspired by DwarfStar4 and Strata, I am trying to build my own gemma4-specific inference engine designed to run on gfx1201.
It is a lot of work. You cannot simply vibecode one in two days.
Subscriptions like these make money by relying on the fact that most people do not try to (or have the time to) extract every last dime out of their plan. The eighty people who do not use up their quota cover for the twenty that do.
That and deals with providers to use spare capacity I guess.
Thanks to competition from open models, there is a limit to how much prices can be increased though.
Unless you are running a swarm of agents, it is extremely difficult to exhaust these plans in a month.
I have been thinking about this for a while in the context of the quest for AGI. If all we want is something to take over some parts of our intellectual labor, then LLMs are already there. They can communicate with you in a language you understand and do stuff that you command them to. Often, on their own.
AGI will have its own telos. An LLM does not (at least I refuse to believe that it does).
So, yeah, you could think of LLMs as programs processing data. But there is definitely some emergent behavior you notice that emerges when the LLM runs. How you interpret all this depends on your view on consciousness and intelligence.
Some systems are fully formed AI. Often based on heuristics. Humans have encoded the rules into the system. Others do limited learning based on pattern recognition. Still others learn based on tagged data. And LLMs don't need even that. In a stream of N tokens, the trainer has access to K+1 when it is training a model that knows 1...K and can check if the prediction was correct.
AI literally refers to any artificial thing that feels intelligent to us: does some thing a simple bash script will not. Your rules based chess engine is AI. The bot you compete against in single player Age of Empires 2 is AI. DeepBlue was AI.
ML is where things become interesting because we teach machines to learn.
NN is where we borrow terminology from neuroscience and create "neurons" that learn.
DL is where we stack neurons deeply in layers.
I am trying to make sense of all this myself.[1] So I am building a curriculum for it as I learn. So much of the teaching material in the wild revolves around torch. Makes it useless if you want to peek inside the actual machinery.
Student of संस्कृतम्
Sanskrit reader at https://www.adhyeta.org.in/
Devlog at https://bhashika.org.in/