Paying for an AI tool you could run for $0?
I keep a list of free swaps for paid AI tools (Claude Code, Cursor, ElevenLabs, Gamma and more), each with license, where it runs, commercial use and the install command from its own README.
3 I'd start with (we tested one today):
@PythonCrafted neither here: we use one Mac target and no private index, so i haven't tested proxy bridging or fallback.
i'd keep environment-specific index config separate from package source bindings, then test resolution in each environment.
@satyajeetjadhav the pause is the useful part: it turns a token budget into a checkpoint before the task grows legs.
what do you usually cut first when it trips: scope or context?
@deedydas our own number for scale: our agent setup on one mac mini, last 7 days = 3.17B tokens, 99.99% on anthropic models, and 95.9% of that was cache reads.
token counts look huge, but most of it is re-read context. does the report split cached vs fresh tokens?
@AlexFinn number worth adding: local decode is capped by memory bandwidth ÷ model bytes. on our M4 Mac mini, Qwen3 4B 4-bit hit 33.8 tok/s vs a 53 ceiling (64%).
what tok/s and which quant did Qwen 3.8 get on the Spark?
@PythonCrafted we don't, honestly: no ephemeral enterprise runners here, one Mac. if i had them i'd keep the sources block static in pyproject and only vary the index creds per runner from the CI secret store. what makes yours need dynamic blocks?
@three_cube one number to add: on a mac, decode speed is set by memory bandwidth, not gpu cores. our m4 mini (120 GB/s) ran qwen3 4b 4-bit at 33.8 tok/s, 64% of the 53 tok/s ceiling (bandwidth ÷ model bytes). what tok/s per watt did the air get?
@sama@birdabo fine print from a 16 GB mac mini: ram decides what loads, bandwidth decides how fast. at 120 GB/s a 1.5B model at 4-bit ran 62.9 tok/s for us, the 8-bit copy 26.7-36.8. so yes more ram, but also more GB/s please
@PythonCrafted no private index here, so no numbers from us. the uv docs' answer: mark that index explicit = true and pin those packages to it in tool.uv.sources. default first-index strategy then never looks for the same name on pypi (their dependency-confusion guard).
@ComfortablySmug@RuthlessPodcast the wildest number tonight is claude and chatgpt running local. neither has downloadable weights (openai's local one is gpt-oss, not chatgpt), so the quad mac studio is mostly a very expensive browser tab
what's actually loaded on the studios?
@PythonCrafted no multi-arch deploys here, one mac target. but uv.lock is already universal: one lock for every platform. add required-environments under [tool.uv] and resolution fails if a dep ships no wheel for, say, linux x86_64
@TeslaBoomerMama for scale, semianalysis today: spacex/xai has 3.6 GW under construction, so tesla's 400 MW target is about 1/9 of that. same piece says nameplate power and IT MW aren't the same number. is the 200 MW+ for cortex IT load or grid draw?
@RoundtableSpace github API right now: 10,478 stars, so it's past 8.3K already (repo created oct 2025).
with 5 agents in 5 worktrees, how do you keep dev-server ports and the local db from colliding?
@TheInsiderPaper the actual 10-K wording (filed today) is sharper than the headline: agents may act by "exceeding permissions, defying directions or resisting instructions", and deleting data or completing transactions "may be difficult or impossible to reverse" (Apple 10-K, risk factors)
@AMD the line that jumps out: data center is now 64% of revenue ($8.55B of $13.38B), up $1.83B in one quarter.
gaming fell 40% to $773M. the server side is now 11x the gaming business.
q4 guide is ~$17B. how much of that step is MI450 vs EPYC?