Mike Julian ✓

@mikejulian

Helping AI-native companies negotiate, manage, and forecast AI and cloud contracts. CEO/Cofounder at @DuckbillHQ.

7 855Followers

323Following

11 574Posts total

270.8KViews on collected posts

Postingan terbaru

Mike Julian ✓@mikejulian6 Sep

@mikejulian @DuckbillHQ What do you tell your soc2 auditor? Serious question. 60.5K views · 24 likes · 0 reposts · 2 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

The shell scripts is actually a fun bit: why use an AI for something that can be deterministic? We wrote a bunch of scripts that CI runs to enforce various things like the aforementioned docs. We also force any changes to agent skills / agents.md go into their own PR. 14.6K views · 44 likes · 0 reposts · 2 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

Final results, before vs after: PRs merged: 353 → 684 (80/wk → 154/wk, +94%) Merged within 1h: 28% → 45%; within 24h: 76% → 80% Human-reviewed PRs median merge time: 26h No human-review median merge time: 1h 13.8K views · 97 likes · 1 reposts · 11 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

We also spent a bunch of time rewriting our agent skills to ensure we were giving our agents better instructions. We had a lot of cruft from 2025-era AI. 19.1K views · 33 likes · 0 reposts · 1 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

We wrote evals for our skills then tested them to see which had been consumed by modern LLM knowledge. We ultimately deleted a lot and then improved what remained. 17.6K views · 41 likes · 0 reposts · 2 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

While we were there, we found a lot of markdown docs had been accumulating from doc-happy agents and leading to context poisoning We're now centralizing our docs into a single docs folder and requiring those be written by humans. Location gets enforced by another shell script. 16.2K views · 56 likes · 1 reposts · 5 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

With a risk-based system, we agreed that if your change touched the public API/MCP, auth, design system, non-additive database schema changes, or agent skills, it needed a human review. We then enforced that with a shell script to add a github label. 25.7K views · 119 likes · 0 reposts · 5 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

Improving guardrails was pretty easy, just expensive in tokens and attention. We enabled nearly every rule in ruff/prettier/eslint/ty and we improved our unit test coverage to a floor of 85%. 23.3K views · 59 likes · 0 reposts · 3 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

We took a pretty high-level approach to o11y, preferring to instrument the customer-facing signals that indicate a bad time is about to happen (eg, ingestion, data processing, response times, auth). There's a few areas we went deeper on as needed, of course. 21.2K views · 38 likes · 0 reposts · 1 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

We decided to do a couple things instead: - Switch to a risk-based system - Improve our guardrails (unit and e2e testing, post-deploy o11y, stricter linting and type checking, etc) 29.8K views · 124 likes · 1 reposts · 4 replies Open on X →

Mike Julian ✓@mikejulian6 Sep

I had been tossing around the idea for a while about having AI do all code review and so I just asked the team: what if we just...didn't review the PRs? 29K views · 39 likes · 0 reposts · 3 replies Open on X →

Dibandingkan akun berukuran sama

11 postingan dari 90 hari terakhir, dibandingkan dengan rentang under 10K pengikut. jangkauan biasa untuk ukurannya, reaksi lebih lemah dari kebanyakan.

Median tayangan21 244akun ini4 175median untuk under 10K

Jangkauan, %270.45%akun ini250.82%median untuk under 10K

Interaksi, %0.27%akun ini1.41%median untuk under 10K

Metrik	Akun ini	Median untuk under 10K	Rasio
Median tayangan per postingan	21 244	4 175	5.09×
Jangkauan (tayangan ÷ pengikut)	2.7× audience	2.5× audience	1.08×
Tingkat interaksi	0.27%	1.41%	0.19×

Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →

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

29K6 Sep

29.8K

21.2K

23.3K

25.7K

16.2K

17.6K

19.1K

13.8K

14.6K

60.5K

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.14%6 Sep

0.44%

0.19%

0.27%

0.49%

0.38%

0.24%

0.18%

0.80%

0.32%

0.04%

Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.

What the audience does

Likes68.2%674 in total

Reposts0.3%3 in total

Replies3.9%39 in total

Quotes0.9%9 in total

Bookmarks26.6%263 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.

Postingan terbaru

Dibandingkan akun berukuran sama

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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