The origin story starts with this looongstanding open issue on TSDX from @n_moore. As an early helper on TSDX I shied away from it bc I thought that it should be solved by dedicated tooling like @yarnpkg.
Big monorepo shops like Facebook and Google have loads of tooling to make monorepos work, but this class of tooling never made it out into open source.
@TurboRepo does 3 things:
- Make monorepos zero config
- Make monorepos incrementally adoptable
- Make monorepos scale
- Read your build pipeline from `turbo` config in package.json
- Generate dependency graph
- Fingerprints inputs
- Executes each task in turn
- Caches output/logs
On subsequent runs, if a task matches a fingerprint, it restores from cache and replays logs.
The API surface area of @TurboRepo is shockingly small:
1️⃣`npx create-turbo@latest myrepo` scaffolds a new monorepo
2️⃣`turbo run build` runs the `build` task
3️⃣configure pipeline in package.json `turbo` key
The showstopper, still in beta, but the reason why @Vercel's acquisition makes total business sense (apart from gaining the imprimatur of having @jaredpalmer on staff).
- Efficient scheduler + rebuilder system ensures you never recompute work that has been done before
- Parallelize as much as possible
- 74% written in Go (see: "Systems core, Scripting shell" in Third Age thesis)
- "Zero config" - lots of value out of the box
- Declarative build pipeline
- Great debugging/profiling
- Great docs/marketing
- Devs have been hearing nonstop about the benefits of monorepos but always held back by tooling
For more on TurboRepo, including notes on future roadmap, check out my blogpost:
As a former monorepo skeptic, IMO this is the time to really dig into them as they are set to explode in 2022.
Added a section to give some spotlight to @NxDevTools as well, as @jeffbcross has been patiently answering questions in the replies. They have a comparison page up on their docs: nx.dev/l/r/guides/tur…
Here’s my @OpenAIDevs day thread for those following along. everyone else gotchu with videos and stuff so i will just give personal notes and aha moments thru the day
after some nice screenshot of Cocounsel, time for @romainhuet’s legendary live demos. o1 one-shots an ios app and does the frotnend/backend to control a drone.
ai controlled drones, what could go wrong?
@romainhuet Realtime API announced!
starting with speech to speech support
all 6 adv voice mode voices supported
just realized NotebookLM is @GoogleDeepMind's ChatGPT moment
- "low key research preview"/"experimental"
- not monetized
- GPUs/TPUs immediately on fire
- SOTA proprietary new model buried in there with upgrade that weren't previously announced
- new AI UX that cleverly embeds LLM usage natively within the product features
in this case NBLM nailed multimodal RAG and I/O in a way that @ChatGPTapp never did (or for that matter, @GeminiApp). The multiple rounds of preprocessing described by @stevenbjohnson also raise the quality of the audio conversation dramatically at the cost of extreme latency (took an efficient model that was advertised as capable of generating 30s of audio in 0.5s, and slapped on like 200s of LLM latency haha)
@GoogleDeepMind like, i put my podcast into it and it made a podcast of my podcast and... it was good.
do u guys know we spend 1-2 hrs writing up the show notes and now its a button press in NBLM
Gemini really took pride topping @lmsysorg for a hot second and then @OpenAI said "oh no u dont" and put out 4 straight bangers pounding everyone into the dust by 50 elo points
V high bar set for Gemini 2, Grok 2.5, and Claude 4 this fall.
Multiple fronts - on reasoning, multiturn chat tuning, instruction following, and coding - to compete.
anyway we finally did a @latentspacepod paper club on STaR and friends, swim on by
i hastily sketched out a "paper stack" of what the "literature of reasoning" could look like, but this is amateur work - would love @teortaxesTex or @arattml to map out a full list of likely relevant papers for o1
holy shit @ideogram_ai thumbnails are untapped alpha
notable reveals from today's iphone 16 event, especially Apple Visual Intelligence:
- Mail and Notifications will show summaries instead of str[:x]
- Siri now knows iPhone, becomes the ultimate manual on how to use the increasingly complicated iOS 18
and can read your texts (!) to suggest actions with Personal Context Understanding
(also it will try to advertise apple tv shows to you... i'm SURE it will be totally objective and aligned to your preferences amirite)
- new iphone 16 camera control button is PRIME real estate - notice how OpenAI/ChatGPT is now next to Google search, and both are secondary clicks to Apple's visual search, which comes first
- camera adds events to calendar!
"all done on device" and on cloud (though craig doesnt say that haha)
Llama 3 paper is finally out! by @lvdmaaten and Angela Fan. Quick diffs from yesterday's leaks (+ watch our exclusive @ThomasScialom interview out now!)
- NEW SCALING LAWS! turns out there's a reason why they trained a 405B param model because they had 15T tokens
- full weight class benchmarks table vs Gemma, Mistral, 4o/sonnet! no surprises - 8B and 70B are strongest here, but 405B has solid IFEval and Tool Use
- Multimodal encoder, Vision and Speech Adapter coming
- 15T token data pipeline uses Llama 2 cleaning/filtering, and Deepseek v2 pipelines for code and math!
some pretty fun notes on infra and training - together with full details on learning rates and training recipe.
this is going to make @Teknium1 happy - 3 approaches for syndata explored, apart form the obvious 8B/70B distillation
- 405B teaching itself with code execution feedback
- translating code data to smaller programming languages (like TypeScript and PHP??? this is slander)
- "backtranslation" - 1.2m synthetic dialogs going from documentation/explanations to code, then using LLM as judge to filter (pretty smart!)