Girish Mathrubootham ✓

@mrgirish · Palo Alto, CA · joined 23 Aug 2008

Founder & Executive Chairman - Freshworks Inc(Nasdaq: FRSH) AI investor & founding partner https://t.co/2x1N7TAYs2

58 273Followers

386Following

1 427Posts total

129.7KViews on collected posts

Derniers posts

Girish Mathrubootham ✓@mrgirish17 Aug

As investors, we look for great founders, big markets, and the ambition to build something category-defining. Wispr has all three. Today, we’re excited to announce Together Fund’s small strategic investment in Wispr Flow’s Series B, backing Tanay Kothari, Sahaj Garg and the team 23.9K views · 165 likes · 8 reposts · 7 replies Open on X →

Girish Mathrubootham ✓@mrgirish4 Jun

A new chapter for Together — and a good one. We set out to back exceptional founders building AI-native companies from India for the world — and a few years in, that portfolio has become something we're deeply proud of. A family of companies compounding faster than we imagined, 12.9K views · 69 likes · 5 reposts · 8 replies Open on X →

Girish Mathrubootham ✓@mrgirish18 May

The AI battle is moving from Who has the best foundational model —>who has the best harness —> who has the best memory and context? Glad to see @scaletogether portfolio company @sentra_app taking the lead here !! 13K views · 34 likes · 3 reposts · 4 replies Open on X →

Girish Mathrubootham ✓@mrgirish30 Mar

I rarely get into twitter (X) debates. As a founder I have always wondered how so many outsiders have opinions about a business. With the @emergentlabs revenue rate debate - here are a few points to consider 1. A private company does not need to disclose any numbers and nobody

30.2K views · 205 likes · 18 reposts · 18 replies Open on X →

Girish Mathrubootham ✓@mrgirish26 Mar

@mrgirish Ppl expect probabilistic LLMs to some deterministic problems. That won’t happen. To make LLMs work in deterministic world you need a lot of guardrails and controls to make LLM work and filter out errors . Intelligence many cases that is more software than the software 603 views · 3 likes · 1 reposts · 0 replies Open on X →

Girish Mathrubootham ✓@mrgirish26 Mar

@mrgirish The agent is the brain, the SaaS is the memory - that framing is right. But there's a layer missing between the two: intent. Agents can query Jira. They can read Slack. They can pull CRM data. What they can't do is tell you which of the 47 open tickets actually matter 182 views · 1 likes · 0 reposts · 0 replies Open on X →

Girish Mathrubootham ✓@mrgirish26 Mar

@mrgirish if an agent sits on top of freshworks of salesforce, that definitely doesn't kill SaaS. but if you need only one license and agent work on top of that license, then you can't sell seats to enterprise. just one license can be integrated with agents. that is bigger worry. 717 views · 5 likes · 1 reposts · 2 replies Open on X →

Girish Mathrubootham ✓@mrgirish26 Mar

Anthropic’s agents won’t kill SaaS. But something else will. The “SaaSpocalypse” narrative is everywhere right now. When Anthropic dropped Claude Cowork plugins in Feb, $285 billion+ evaporated from software valuations in 48 hours. I’ll be the first to admit: I have skin in the 48.2K views · 486 likes · 78 reposts · 51 replies Open on X →

Face aux comptes de taille comparable

1 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.

Vues médianes23 938ce compte966médiane pour 10K–100K

Portée, %41.08%ce compte3.66%médiane pour 10K–100K

Engagement, %0.76%ce compte1.51%médiane pour 10K–100K

Indicateur	Ce compte	Médiane pour 10K–100K	Rapport
Vues médianes par post	23 938	966	24.8×
Portée (vues ÷ abonnés)	41.08%	3.66%	11.2×
Taux d'engagement	0.76%	1.51%	0.51×

Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →

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

48.2K26 Mar

717

182

603

30.2K30 Mar

13K18 May

12.9K4 Jun

23.9K17 Aug

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.29%26 Mar

1.12%

0.55%

0.66%

0.82%30 Mar

0.32%18 May

0.63%4 Jun

0.76%17 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.51%.

What the audience does

Likes59.3%968 in total

Reposts7.0%114 in total

Replies5.5%90 in total

Quotes1.1%18 in total

Bookmarks27.1%442 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.

Derniers posts

Face aux comptes de taille comparable

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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