Rithwik Pattikonda

Founder @ ClawGTM ( YC S24, ZFellows) Prev founded @octoshop_plugin (acquired for 7 fig all cash), @travelarrow ($1.5M+ ARR),

[Rithwik Pattikonda @rithwikpat]

Roger that!

[Y Combinator @ycombinator]

Roger is an AI SDR that automates outbound sales and follows up 24/7.

Connect your email and LinkedIn— Roger scans 300M+ profiles, writes & sends personalized cold emails & LinkedIn messages, follows up, and books meetings. You just show up and close.

https://t.co/mDvstnmiJh

[Rithwik Pattikonda @rithwikpat]

Skill issue

[Business Insider @BusinessInsider]

Uber COO Andrew Macdonald said he's not seeing proportional productivity gains from increasing AI costs. https://t.co/1VDp9cAqTw

[Rithwik Pattikonda @rithwikpat]

Childhood best friend gonna lose much more than 27k on his next 0DTE trade. This win is probably the worst thing that could happen to him

[Vincent Delisi @ivincentdelisi]

Childhood best friend made $27k in 3 minutes today on $SPY 0DTE calls

The power of options man….

[Rithwik Pattikonda @rithwikpat]

@codyschneider FBManager

[CG @cgtwts]

We just built a money printer

you just paste in your company website and turn it into a fully automated outbound engine.

- It reads your site,
- figures out who should be buying,
- finds matching companies,
- reaches out to them automatically.

[Rithwik Pattikonda @rithwikpat]

@rmrf_404 @cgtwts What’s your company url? Perhaps it got confused with another noticed

[CG @cgtwts]

Check this out: https://t.co/GcMl518JOv

[CG @cgtwts]

Take a look at what we’ve been building:

https://t.co/GcMl518JOv

[Rithwik Pattikonda @rithwikpat]

@paulswaney3 ironically, i wouldnt hire YOUR ass because of this dumbass post

[Rithwik Pattikonda @rithwikpat]

@everestchris6 HOTEL

[Aman @Amank1412]

BRO WHATTT!!!

there’s a tool literally acting like a sales team

it’s called moneyprinter

you just paste your website
and it starts bringing you customers

no list building
no copywriting
no campaign setup

it handles the full flow:

→ finds companies already in-market
→ identifies the exact decision makers
→ writes hyper-personalized outreach
→ sends emails + linkedin + even calls

i tested it and it surfaced accounts i would’ve never found manually

most people are still grinding outbound manually

this just skips all of that

input: your website
output: pipeline

7 day free trial

(link below)

[Rithwik Pattikonda @rithwikpat]

@kcimc @DrewTAmato you would only need to fire the sms notif during the apocalypse so it wouldnt really cost you anything lol. Unless the apocalypse happens in which case $100 for sms costs is the least of your worries

[Rithwik Pattikonda @rithwikpat]

@mikefutia ugc

[Rithwik Pattikonda @rithwikpat]

@MichLieben ads

[theo 👨‍🎨 @theootu]

My design 🆚 ChatGPT

I am not afraid for my job 🤣

[Rithwik Pattikonda @rithwikpat]

atp I would pay to NOT be on Forber 30 under 30

[ocr @onchainrapist]

ben pasternak is going to jail

[sid @sid_mnk]

We're open-sourcing PulseBench-Tab, a frontier benchmark for table extraction.

Table parsing remains one of the hardest and most poorly measured problems in document intelligence. TEDS operates on DOM trees and conflates HTML formatting conventions with structural errors. Needleman-Wunsch linearizes a two-dimensional structure into a one-dimensional sequence, so column transpositions can still score well because values align with nearby cells. GriTS uses greedy grid matching rather than optimal assignment and does not distinguish edge directions. The upshot: existing metrics cannot reliably separate content errors from structural errors, which makes provider comparisons noisy and downstream reliability unknowable.

Alongside the dataset, our research team developed T-LAG. It parses each table into a cell-position grid, emits directed RIGHT and BELOW adjacency edges (suppressed within spanning cells, deduplicated by source, target, and direction), weights each candidate edge pair by the product of Levenshtein-derived similarities on source and target text, and uses the Hungarian algorithm for globally optimal one-to-one assignment. The F1 over matched edge weight is the T-LAG score. Structure and content are evaluated in one unified pass. HTML formatting choices do not affect the result. Rankings are invariant to the similarity exponent across k ∈ {7, 8, 9, 11}.

The dataset contains 1,820 human-annotated tables across 9 languages and 4 scripts (Latin, CJK, Arabic, Cyrillic), drawn from 380 real-world financial filings, government reports, and regulatory disclosures. Tables range from 2 to 1,183 cells; 48.1% contain merged or spanning cells. Ground truth was produced through 8 annotation rounds with native speakers per language, independent cross-lingual review, and adversarial cell-by-cell audits against source images.

We evaluated 9 commercial and open-source systems independently across the full dataset under exclude-missing scoring. Selected findings:

@Pulse__AI Ultra 2 scores 0.9347 T-LAG; the next closest system scores 0.8155. Pulse Ultra 2 is the only provider with a median of 1.0, corresponding to perfect extraction on 57.9% of samples.
Non-Latin scripts produce the widest cross-provider variance. On Arabic, the spread between top and bottom systems exceeds 75 percentage points.

Structural hallucinations are pervasive. The second-ranked system achieves a perfect-extraction rate of 28.6%, meaning structural or content errors on 71.4% of tables (fabricated rows, invented content, incorrect span attributes, shifted data).

Coverage failure is underreported. Multiple evaluated systems return no output on 19% to 21% of samples. Raw accuracy numbers without coverage disclosure favor selection bias.

Thank you to Dushyanth Sekhar and Mohammed Hadi of S&P Global's Enterprise Data Organization for their academic contributions to the benchmark methodology.

Dataset: https://t.co/OzKclS0T14
Evaluation: https://t.co/aIKmudthy1
Blog: https://t.co/0my46Q9KfV
Research methodology: https://t.co/7UDzEx4NeS
Viewer: https://t.co/fh1IQOTElA

[Rithwik Pattikonda @rithwikpat]

@sid_mnk HUGE!

[Rithwik Pattikonda @rithwikpat]

@sid_mnk @Pulse__AI Huge

[Rithwik Pattikonda @rithwikpat]

Their fate was sealed three days ago

[Ceci Stallsmith @CeciStalls]

Lovable is in the Forbes AI 50 this year.

We’re honored by the recognition. It’s motivation to keep building.

Now, back to work.