Tech with Mak ✓

@techNmak · 📩 for collaboration · joined 30 Jul 2024

AI, coding, software, and whatever’s on my mind.

43 287Followers

880Following

5 429Posts total

357.2KViews on collected posts

Against accounts of the same size

7 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.

Median views11 843this account1 045median for 10K–100K

Reach, %27.36%this account3.41%median for 10K–100K

Engagement, %1.85%this account2.02%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	11 843	1 045	11.3×
Reach (views ÷ followers)	27.36%	3.41%	8.02×
Engagement rate	1.85%	2.02%	0.92×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

61.7K4 Sep

73.7K

3255 Sep

11.8K

576

207.6K

1.4K6 Sep

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.46%4 Sep

3.13%

1.85%5 Sep

3.43%

1.56%

0.94%

3.60%6 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 2.02%.

What the audience does

Likes42.5%4 954 in total

Reposts4.7%548 in total

Replies0.8%97 in total

Quotes0.3%36 in total

Bookmarks51.6%6 012 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

Followers by day

5 Sep

Daily snapshots since 05 Sep 2026; the dashed line is the starting count.

Latest posts

KV cache is one of the most important ideas in LLM inference, but it is often explained too casually. During autoregressive generation, a model produces one token at a time. Without caching, each new decoding step would repeatedly recompute key and value states for tokens the ht 1.4K views · 41 likes · 9 reposts · 2 replies 06 Sep 2026 · Open on X →

At some point in your career, you meet an engineer who looks lazy until you realize he is just playing a different game. You give him a problem that sounds like a day of work, he asks a few irritatingly obvious questions, searches the codebase for ten minutes, removes most of htt 207.6K views · 1.8K likes · 104 reposts · 25 replies 05 Sep 2026 · Open on X →

@techNmak I bet Nvidia can also send their own inference jobs to whichever machine in YOUR household has capacity. 576 views · 8 likes · 0 reposts · 1 replies 05 Sep 2026 · Open on X →

Genuinely one of the best engineering books ever written. https://t.co/vQCXjT45Kb 11.8K views · 371 likes · 18 reposts · 15 replies 05 Sep 2026 · Open on X →

@techNmak Sure will be an even bigger unlock for us to be able to sell our surplus compute network capacity for LN sats through a Data Vending Machine using @OpenAgentsInc Infrastructure! 325 views · 4 likes · 1 reposts · 1 replies 05 Sep 2026 · Open on X →

I put together a handbook on how attention works, from the Q/K/V equation up through how it actually runs in modern LLMs. Self vs. cross-attention, causal masking, multi-head attention, RoPE, KV caching, MHA/MQA/GQA/MLA, FlashAttention, worked through by hand, with citations htt 73.7K views · 2K likes · 301 reposts · 23 replies 04 Sep 2026 · Open on X →

NVIDIA just made all the spare AI compute in your house useful. Install PAIR on your PCs and Macs, and local AI agents can automatically send each inference job to whichever machine has capacity. NVIDIA Personal AI Router (PAIR) is a local inference router that lets multiple ht 61.7K views · 746 likes · 115 reposts · 30 replies 04 Sep 2026 · Open on X →