Day 578: Perpl Week 7 points update.

My market-making bot is currently running at a cost of $77 per $1M in volume.

For every $1M traded, I received:
> 62 Points
> 63 mPoints
> 20$/ T2 fee rebate

After accounting for the mPoints and rebate rewards, my net farming cost is just $0.31 per $1M in volume.

That means I'm effectively farming Perpl at $0.005/point

My bear-case valuation is $5 per point, which implies a potential 1000x from my current cost.

In my opinion, this remains the best risk/reward airdrop opportunity in the current bear market.

Link: https://app.perpl.xyz/trade?ref=hWxdtzvyzKw

AFFiNE is an open-source, local-first workspace that merges docs, whiteboards, and databases into one hyper-fused platform.

- Edgeless canvas for any block: rich text, sticky notes, embedded pages, shapes, and slides
- Multimodal AI partner for writing, presentations, mindmaps, and prototyping
- Local-first data ownership with real-time sync and collaboration across platforms
- Self-hostable and forkable, with plugin and third-party block support coming

As an AI engineer, please learn:

- Learn the roofline model and why decode is memory-bound
- Deploy vLLM and SGLang, then read their schedulers
- Understand paged attention from the code, not the blog post
- Build observability before you optimize anything
- Track TTFT, inter-token latency, throughput, queue depth
- Use Grafana + Prometheus for inference dashboards
- Turn on prefix caching and find which workloads it helps
- Learn continuous batching and chunked prefill
- Run load tests with 1000+ concurrent requests
- Report p50, p95, p99, never just the mean
- Master quantization tradeoffs (FP8, INT4, AWQ, GPTQ)
- Learn speculative decoding and where it stops helping
- Set up KV cache eviction for long contexts
- Try disaggregated prefill and decode serving
- Learn Kubernetes for AI workloads and autoscale on queue depth
- Learn how inference costs break unit economics
- Build your own model router by cost, latency, quality
- Create a token budgeting system per request
- Build one inference service and benchmark it publicly
- Read inference research instead of model release news
- Start sharing your optimization benchmarks

I put together a 10-week plan that covers every one of these at 30 minutes a day. It is 50 sessions, split between reading the theory and building on your own service, and all of them feed one artifact: an inference service you deploy, instrument, load test past 1000 concurrent requests, tune, and publish as a reproducible benchmark.

It is open on GitHub and contributions are welcome, especially newer sources worth adding. I am working through it myself and will share more content on this going forward, so stay tuned.

GitHub repo: https://github.com/patchy631/time-to-first-token

(don't forget to star 🌟)

Big monolith = low cortisol https://x.com/systemdesignone/status/2083915791787757860

Typical <60B models