Nikki Siapno ✓

@NikkiSiapno · LUC System Design Newsletter → · joined 06 Apr 2022

Eng Manager | ex-Canva | 450k+ audience | Helping you become a great engineer and leader

176 365Followers

330Following

10 146Posts total

76.3KViews on collected posts

Latest posts

Nikki Siapno ✓@NikkiSiapno11 Sep

@NikkiSiapno Strong distinction. I’d add one layer between guardrails and governance: authority. A guardrail can block an action. Governance can establish accountability. But the system still needs an explicit answer to: Was this exact action authorized, under these conditions, 4 views · 0 likes · 0 reposts · 0 replies Open on X →

Nikki Siapno ✓@NikkiSiapno11 Sep

𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝘀 𝘃𝘀 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝘃𝘀 𝗖𝗮𝘀𝘀𝗮𝗻𝗱𝗿𝗮. Choosing the right database isn’t about the tool. It’s ultimately about the workload. AWS Next Gen Stats is a great real-world example. As Next Gen Stats evolved, it ended up using all three for different workloads. https://t.co/ZIWBmuI81t

GIF

10.9K views · 230 likes · 44 reposts · 4 replies Open on X →

Nikki Siapno ✓@NikkiSiapno11 Sep

@NikkiSiapno Nice 76 views · 0 likes · 0 reposts · 0 replies Open on X →

Nikki Siapno ✓@NikkiSiapno10 Sep

@NikkiSiapno Very useful 150 views · 0 likes · 0 reposts · 0 replies Open on X →

Nikki Siapno ✓@NikkiSiapno10 Sep

@NikkiSiapno the interface problem underneath this is that dashboards show three separate views and leave the joining to a human at 3am. the tool that wins will present one timeline, not three tabs. 17 views · 0 likes · 0 reposts · 0 replies Open on X →

Nikki Siapno ✓@NikkiSiapno10 Sep

𝗟𝗼𝗴𝘀 𝘃𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘃𝘀 𝗧𝗿𝗮𝗰𝗲𝘀. Logs, metrics, and traces can all point to the same problem, but they show you that problem from completely different perspectives. 𝗟𝗼𝗴𝘀 = “𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱?” When something happens inside an application, logs https://t.co/IHDkdZ3JJM

GIF

23.6K views · 626 likes · 119 reposts · 5 replies Open on X →

Nikki Siapno ✓@NikkiSiapno10 Sep

@NikkiSiapno Thanks 38 views · 0 likes · 0 reposts · 0 replies Open on X →

Nikki Siapno ✓@NikkiSiapno10 Sep

AI Guardrails vs AI Governance. 𝗔𝗜 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 are built to control what an AI system can do. They can filter risky prompts, block sensitive data, restrict what information can be retrieved, or limit which tools and APIs an agent can use. That makes them useful for https://t.co/u

GIF

8.1K views · 125 likes · 29 reposts · 9 replies Open on X →

Nikki Siapno ✓@NikkiSiapno9 Sep

@NikkiSiapno what connects them is the inventory: a row per system with owner, decisions it influences, risk tier, last reviewed. acceptable risk is set per system. repoint a model at a higher stakes call and the guardrails never learn the tier moved. only an inventory review ca 27 views · 0 likes · 0 reposts · 0 replies Open on X →

Nikki Siapno ✓@NikkiSiapno9 Sep

AI Guardrails vs AI Governance 𝗔𝗜 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 are built to control what an AI system can do. They can filter risky prompts, block sensitive data, restrict what information can be retrieved, or limit which tools and APIs an agent can use. That makes them useful for https://t.co/xI

GIF

16.6K views · 172 likes · 45 reposts · 3 replies Open on X →

Nikki Siapno ✓@NikkiSiapno8 Sep

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘃𝘀 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘃𝘀 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗮𝗹 𝘃𝘀 𝗘𝗽𝗶𝘀𝗼𝗱𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗠𝗲𝗺𝗼𝗿𝘆 = “𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗵𝗮𝘀 𝗶𝗻 𝗺𝗶𝗻𝗱 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄.” Working memory (aka short term memory) holds the active context needed for the https://t.co/RkW7UxJE9a

GIF

6.3K views · 81 likes · 24 reposts · 5 replies Open on X →

Nikki Siapno ✓@NikkiSiapno7 Sep

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 𝗧𝘆𝗽𝗲𝘀 𝗖𝗹𝗲𝗮𝗿𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗠𝗲𝗺𝗼𝗿𝘆 = “What the agent has in mind right now” Working memory (aka short term memory) holds the active context needed for the current task: recent messages, goals, tool outputs, https://t.co/KBOnC6ZIwR

10.4K views · 40 likes · 11 reposts · 8 replies Open on X →

Against accounts of the same size

12 posts from the last 90 days, next to the 100K–1M follower range. ordinary reach for its size, weaker reaction than most.

Median views3 242this account4 060median for 100K–1M

Reach, %1.84%this account1.59%median for 100K–1M

Engagement, %0.28%this account1.32%median for 100K–1M

Metric	This account	Median for 100K–1M	Ratio
Median views per post	3 242	4 060	0.80×
Reach (views ÷ followers)	1.84%	1.59%	1.16×
Engagement rate	0.28%	1.32%	0.22×

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

10.4K7 Sep

6.3K8 Sep

16.6K9 Sep

8.1K10 Sep

23.6K

150

7611 Sep

10.9K

Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.57%7 Sep

1.74%8 Sep

1.32%9 Sep

0.00%

2.00%10 Sep

0.00%

3.18%

0.00%

0.00%11 Sep

2.54%

0.00%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.32%.

What the audience does

Likes46.7%1 274 in total

Reposts10.0%272 in total

Replies1.2%34 in total

Bookmarks42.1%1 149 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

Against accounts of the same size

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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