Matei Zaharia ✓

@matei_zaharia · Berkeley, CA · joined 11 Oct 2010

CTO @Databricks and prof @UCBerkeley. Working on data + AI, @ApacheSpark, @DeltaLakeOSS, @MLflow, @DSPyOSS, @GEPA_ai, @Omnigent_ai.

52 062Followers

1 492Following

3 465Posts total

531.2KViews on collected posts

Against accounts of the same size

15 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views15 000this account1 384median for 10K–100K

Reach, %28.81%this account4.31%median for 10K–100K

Engagement, %0.68%this account1.92%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	15 000	1 384	10.8×
Reach (views ÷ followers)	28.81%	4.31%	6.68×
Engagement rate	0.68%	1.92%	0.35×

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

242.5K13 Jun

6.3K

9.7K

30.3K

6.9K

7.9K

3K14 Jun

34.7K26 Aug

22.5K3 Sep

55.6K

53.3K

15K

18.3K4 Sep

11.5K

Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.68%13 Jun

0.38%

0.88%

0.47%

0.48%

0.31%

0.43%14 Jun

0.90%26 Aug

0.43%3 Sep

0.37%

1.03%

0.70%

1.05%4 Sep

0.94%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.92%.

What the audience does

Likes56.6%3 038 in total

Reposts7.2%387 in total

Replies3.3%175 in total

Quotes1.6%87 in total

Bookmarks31.3%1 682 in total

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

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Latest posts

Stronger open models + new inference systems also means super powerful local AI is coming! Cool work by Shuo from UC Berkeley Sky Lab. 11.5K views · 98 likes · 8 reposts · 2 replies 04 Sep 2026 Check out the Lakebase VLDB paper for tons of detail on why and how Neon and Lakebase are built! We think this type of highly elastic architecture over commodity lake storage (S3) is going to be used for more and more infra as software development speeds up and agents do more of 18.3K views · 173 likes · 15 reposts · 4 replies 04 Sep 2026 Really cool example of using tracing data to get insights into AI workloads with Unity AI Gateway. We think this will be a new form of operational data analysis that all companies do, a lot like finance and security. 15K views · 89 likes · 10 reposts · 6 replies 03 Sep 2026 Qwen3.8-Flash-Next now runs on a single RTX 5090 at 68.3 tok/s — no extreme quantization, no speculative decoding. · Production-level checkpoint: GB300-validated NVFP4 checkpoint from RadixArk · 63GB host RAM — less than what 1-bit quants of this model need · The 51GB n-gram h 53.3K views · 463 likes · 37 reposts · 36 replies 03 Sep 2026 https://t.co/uWgWVdMvuj This is the foundation of Lakebase architecture pioneered by @neondatabase We just presented it at @VLDBconf - the main database scientific conference and also covered it on the @VLDBconf keynote by a @databricks cofounder @rxin 55.6K views · 175 likes · 26 reposts · 1 replies 03 Sep 2026 We found and eliminated an estimated $1.2M a year in wasted AI spend and lost productivity in one hour. Here's how 👇 Using Unity Gateway tracing and Genie One, we identified seven small MCP-server bugs that were quietly driving: • ~$499K/year in wasted tokens • ~12,000 https:/ 22.5K views · 80 likes · 7 reposts · 6 replies 03 Sep 2026 This is unfortunate, but this is why we're making cloud storage, databases, etc all branchable and recoverable at @neondatabase and @databricks! Agentic development will need a new kind of cloud infra. 34.7K views · 279 likes · 23 reposts · 10 replies 26 Aug 2026 @matei_zaharia This is really great -- very nice are you accepting PRs? https://t.co/87LkvNJ2dp 3K views · 10 likes · 2 reposts · 1 replies 14 Jun 2026 For security & cost control, Omnigent introduces a *contextual* policy model that can track session state (e.g. ask for permission to git push if an agent downloaded an untrusted npm package, or ask you after every $100 of spend) and a strong OS sandbox from our security tea 7.9K views · 22 likes · 1 reposts · 1 replies 13 Jun 2026 Omnigent can be run on your laptop or deployed to @Docker, @Railway and @flydotio, and can run agents in the cloud on @modal and @daytonaio. It works with any LLM provider too (coding agents' subscriptions, @OpenRouter, @databricks, etc). 6.9K views · 31 likes · 1 reposts · 1 replies 13 Jun 2026 There's a lot more coming, e.g. meta-optimization with @gepa_ai and programmatic tool calling as in RLM and MemEx. Read more in our blog: https://t.co/SYjEZnJpVS or get started at https://t.co/G9tZA0y2X6 30.3K views · 122 likes · 10 reposts · 7 replies 13 Jun 2026 Omnigent focuses on three problems above the level of a single harness: composition, collaboration and control. https://t.co/SYjEZnJpVS For composition, it lets you create multi-agent teams with different harnesses or swap harness and model mid-session and mid-loop. 9.7K views · 71 likes · 10 reposts · 2 replies 13 Jun 2026 For collaboration, Omnigent adds a common UI above any agents, with native app, web and mobile UIs and live collaboration with teammates. No more copy-pasting text and screenshots between agents, Slack, and Docs all the time! You can even review code with the agent that wrote it. 6.3K views · 23 likes · 0 reposts · 1 replies 13 Jun 2026 Really excited to open source a new project: Omnigent, a meta-harness for AI agents. It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies. h 242.5K views · 1.3K likes · 215 reposts · 96 replies 13 Jun 2026 Omnigent is based on the trend we saw with AI usage at Databricks and Neon: engineers were combining multiple agents into loops and workflows, and this was difficult above the harness layer. We add a uniform API above any harness that enables rich features on top. https://t.co/IV 13.6K views · 125 likes · 22 reposts · 1 replies 13 Jun 2026