Hiring in my team at Gushwork.
Associate Program Manager on our website development product.
Think Lovable, but for B2B SMBs. Our AI pipeline (Onboarding Studio) takes a domain to a live site in 30 minutes. You own the loop from customer call to shipped website: steer the pipeline, feed it what it's missing, and ship what it can't.
Manage ops + tech.
Bangalore. Startup pace.
Bar: 1-2 years at an early-stage startup. Steps into the code and knows their shit in Claude Code. Turns a messy customer call into 5 clean lines of spec.
Read the JD before you DM. Been filtering hard.
https://t.co/Mi6kND64pV
#hiring
LLMs are still slow because they generate one token at a time.
That is the bottleneck DFlash tries to attack.
I wrote a blog breaking down **DFlash: Block Diffusion for Flash Speculative Decoding**.
The main idea:
Instead of drafting future tokens one by one, DFlash uses a block diffusion drafter to predict multiple future tokens in parallel, then lets the target LLM verify them.
In the post, I covered:
* speculative decoding basics
* why draft models can still be sequential bottlenecks
* block diffusion drafting
* target hidden features
* KV injection
* reported `6x` lossless acceleration
I haven’t posted much here or written blogs in a while.
Trying to change that now.
I’ve been reading more AI research papers recently, especially around LLM inference, optimization, serving systems, and model efficiency.
Instead of just keeping notes for myself, I want to turn more of them into blogs.
The plan is simple:
- read papers more regularly
- write practical breakdowns
- focus on intuition, implementation, and tradeoffs
- post on both Substack and Medium
If you’re interested in AI research, LLM systems, and ML engineering, follow along.
Substack: https://t.co/x6ZRoaDGAn
Medium: https://t.co/AQTAH0IFvn
#AI #MachineLearning #LLM #DeepLearning #GenAI
sent a mail starting with ‘Sure, here’s a more polished and professional version of your message’ now where do i hide from the face of this earth this is too embarrassing