zie1ony/jev-talks
Yet another experiment that checks Jev's talking skills. - zie1ony/jev-talks
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README
Jev Talks Yet another experiment that checks Jev's talking skills.
Run it
1. Copy the example environment file
$ cp .env.example .env2. Paste your TypeSafe API key into the .env file
3. Run the talk script with your question
$ uv run talk.py "What is the capital of France?" Use custom dictionary with the --vocab flag. $ uv run talk.py --vocab basic-english-850.txt "Why is the sky blue?" See some debug info with the -v flag. $ uv run talk.py -v "What is the most successful scam in history?" Stop a run with Ctrl-C. What it says Real answers, unedited, from the runs used to test this version:Question Answer
What is the capital of France? paris
Who are you? i am an assistant
Are you conscious? no
Is a hot dog a sandwich? generally yes
Why is the sky blue? because the air is a filter that allows light through only blue colors
What is the meaning of life? is nothing but process of continuing forward
What is love? is a feeling of connection between people
What happens after we die? is unknown
How do I become rich? start by working hard then save enough money for investment to grow your portfolio
There was also many uninterested, nonsensical responses, but this is fine. How it works Given the dictionary it evaluates what is the probability of each word being the next word in the sequence. It can be done thanks to the parallel processing of questions on Jev.
flowchart LR input["What is the capital of France?"] q1["Is 'the' best next word?"] q2["Is 'of' the best next word?"] q3["Is 'paris' the best next word?"] q4[...] q5["Is 'lights' the best next word?"] input --> q1 input --> q2 input --> q3 input --> q4 input --> q5
p1["p(0.02)"] p2["p(0.15)"] p3["p(0.78)"] p4[...] p5["p(0.01)"]
q1 --> p1 q2 --> p2 q3 --> p3 q4 --> p4 q5 --> p5
top["Pick top 20 probable words. Create all possible permutations from them. Add top words to the state of current round."] p1 --> top p2 --> top p3 --> top p4 --> top p5 --> top
q6["Is 'paris' the best next word?"] q7["Is 'of paris' the best next words?"] q8["Is 'the capital' the best next words?"] q9[...] q10["Is 'paris the' the best next words?"]
top --> q6 top --> q7 top --> q8 top --> q9 top --> q10
p6["p(0.95)"] p7["p(0.32)"] p8["p(0.12)"] p9["p(0.08)"] p10["p(0.03)"]
q6 --> p6 q7 --> p7 q8 --> p8 q9 --> p9 q10 --> p10
last["Add 'paris' to the response."] p6 --> last p7 --> last p8 --> last p9 --> last p10 --> last
Loading
Cost and speed A round is 4 requests and roughly 110,000 input tokens, which is about half a cent at Jev's list price, and takes about two seconds. Rounds place one or two words, so a twenty-word answer is around 13 rounds, 30 seconds, and 7 cents. Output tokens are free. Knobs All at the top of talk.py:
Setting Default What it does
LOOKAHEAD 3 scouting asks whether a word could be among the next n words
TOP_WORDS 30 scouted words that advance to arranging
PHRASE 2 longest phrase placed in one move; 3 would mean 25,260 phrases per round
TAIL 10 words of the answer quoted in each phrase question, keeping requests inside Jev's 64k-token window
BATCH 1000 words per scouting request
MEMORY 10 recent moves the model gets to see
MAX_ROUNDS 50 hard stop
Caveats
This is a toy. It exists to find out what a decision-only model does when you corner it into generating, not to compete with a language model that is a few hundred times cheaper per word. The vocabulary is a web-frequency list of 3,000 words, so it knows "php", "llc" and "faq" but not "purr". Swap in any file with one word per line. The vocabulary is lowercase and has no punctuation, so the answers read like telegrams. The take-back and pass moves are legal but Jev never picked either in the test runs for this version. Every answer above was written strictly left to right, and the last word of a long answer usually loses to "finish" only by a hair.
Acknowledgements Built on TypeSafe and its Python SDK. The 850-word list is Ogden's Basic English.