Shakthi ✓

@v_shakthi · India · joined 13 Jan 2009

Enterprise AI Architect | Helping C-Suite Leaders De-Risk AI Platforms, Optimize Token Economics & Build Scalable Agentic Architecture. Survived a heart attack.

122 011Followers

94 617Following

482 489Posts total

1.5KViews on collected posts

Latest posts

Shakthi ✓@v_shakthi8 Sep

Repeat after me: #Opus5 is nearly useless at anything other than burning tokens and verbose talking 23 views · 1 likes · 0 reposts · 0 replies Open on X →

Shakthi ✓@v_shakthi8 Sep

One client cut inference costs 62 percent in 90 days using pure FinOps discipline. Here is exactly what we did. We applied quantization and continuous batching first. Then we introduced model routing that sent simple queries to smaller distilled models. Infrastructure 72 views · 0 likes · 0 reposts · 1 replies Open on X →

Shakthi ✓@v_shakthi8 Sep

AI Architect's Daily Briefing - September 8, 2026 The transition away from unconstrained model scaling toward unified agent control planes, hardware-enforced boundaries, and deterministic data infrastructure is defining today's enterprise landscape. Here is my operational https

82 views · 0 likes · 0 reposts · 0 replies Open on X →

Shakthi ✓@v_shakthi8 Sep

UN High Commissioner for Human Rights Volker Turk took the AI risk debate onto the floor of the Human Rights Council in Geneva. He told the 47-member body he shares the concern of industry insiders: advanced AI poses an existential risk to humanity. His demand was specific. Put 136 views · 0 likes · 0 reposts · 0 replies Open on X →

Shakthi ✓@v_shakthi15 Jul

@v_shakthi Would love to talk. Sending you a DM about https://t.co/JM2TQQIQu7 29 views · 0 likes · 0 reposts · 2 replies Open on X →

Shakthi ✓@v_shakthi14 Jul

I’m available for paid reviews or paid second-opinion conversations around #EnterpriseAI, #Architecture decisions, and #AIRisk / #AIGovernance / #TokenEconomics / #AIAgentDeployment questions. If this helps, happy to connect. 1.2K views · 1 likes · 0 reposts · 1 replies Open on X →

Against accounts of the same size

6 posts from the last 90 days, next to the 100K–1M follower range. reaches fewer people than peers of the same size.

Median views77this account5 865median for 100K–1M

Reach, %0.06%this account1.59%median for 100K–1M

Engagement, %0.78%this account0.98%median for 100K–1M

Metric	This account	Median for 100K–1M	Ratio
Median views per post	77	5 865	0.01×
Reach (views ÷ followers)	0.06%	1.59%	0.04×
Engagement rate	0.78%	0.98%	0.79×

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

1.2K14 Jul

2915 Jul

1368 Sep

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.17%14 Jul

6.90%15 Jul

0.00%8 Sep

0.00%

1.39%

4.35%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.98%.

What the audience does

Likes33.3%2 in total

Replies66.7%4 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

Similar accounts