← Back to leaderboard
vasuman

vas

Verifiedactive
@vasuman

CEO @varickagents, Implementing AI at Enterprise. Prev AI @meta

RosterAI Agents & Productivity ToolsStartup & SaaS BuildersCrypto & Web3
107,880 followers · 514 following · Last polled 3h ago
Perf. Score
71.5
Rank #60
Value Score
17
#231 of 273 priced
Median views
11K
p25 7.8K · trailing 7d
Followers
108K
+2.2% 7d
ER (impr.)
2.62%
eng ÷ impressions
ER (foll.)
0.57%
eng ÷ followers
Est. CPM
$205.55
QT-rate basis
WoW views
+48.6%
▲ rising

Rates & value · est. from 11K median organic views

overpriced
Quote tweetbasis
$2250
$205.55 / 1K views
Post
—
no CPM
Thread
—
no CPM
Retweet
—
no CPM
Value Score 17 · rank #231 of 273 priced$7281.55 / 1K engagementsrate age unknown (pre-audit import)

Trends

Follower growth
Median views / day
Engagement rate
View distribution · trailing-7d posts

Each blue dot is one organic post. The Performance Score ranks by the median (11K), not the mean (23K) — so a single viral post doesn't inflate a creator's reach. The p25 floor (7.8K) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
Opus 5.5 just dropped meaning you have one week to squeeze every ounce of intelligence out of it before it gets nerfed down to Haiku 3.5frozen29d ago812K5.1K12382241520.67%
Open the article Cmd A Open Claude Code Cmd V Enter Winfrozen29d ago537K2.0K703234.7K1.27%
https://t.co/fGfB2uWgVE
3d ago
106K
648
38
29
20
1.8K
2.37%
I remember shouting this from the rooftops a year ago when everyone was focused on making more SaaS: AI is a services game. You cannot solve for enterprise inefficiency with tool adoption. You need AI implementers who go in, understand business processes, refactor for an AI-native reality, and then implement custom agents for those requirements. Varick has been building in this domain for longer than anyone. Our client satisfaction and expansion speaks for itself. Meta is just the latest to have entered the arena. I'm glad, because there are a million companies in the US alone that need a service like this, and Varick could never help all of them alone. For companies that don't want to be vendor locked into any one AI company's systems, subjecting themselves to - price hikes - model quantization/regressions - rate limits - migration costs down the line Let's chat. We're also HIRING: - FDEs - Platform Engineers If you're top 1% in your field, I want you at the #1 AI Transformation company in the world.frozen23d ago88K718315937381.75%
Broke down what it means to actually do AI Transformation work, how to be an effective FDE, and what workflows we've seen at the enterprise level. Give it a listen and let me know what you think. Will make more content like it if people find it helpful. Thank you Greg for having me on!frozen21d ago56K508243218982.64%
HIGH PRIORITY HIRE @ VARICK: Forward Deployed GTM Engineer We have ~$600M in pipeline and not enough people to close it. New leads come in every day from a mix of sources (X, our newsletter, email, DMs, etc.). You'll be responsible for wrangling this pipeline: outbound, inbound, and even outbound on hiring (an extension of selling). You'll also have a huge impact on our messaging, marketing, design, and product roadmap, since you'll be on calls every week with dozens of the largest companies on earth, from the Fortune 500 up to the Fortune 10. Applied AI is still early, and we're going to dominate it. You'll be central to driving the next 9 figures of revenue at Varick. Comp: $150K-$200K base, up to 1% equity, and commission on every deal you work, up to $1M a year. Have you done at least one of the following? - Finished top 10% of your SDR, BDR, or AE team for 2+ quarters - Spent 2+ years at a top bank, consulting firm, or PE fund with strong performance - Started something of your own that made $100K+ in revenue - Built your own outbound engine (Clay, scrapers, agents) and can show the reply rates If you're a mercenary who's ready for long, intense hours, apply below or DM me. $20k bonus if we hire someone you send my way.frozen14d ago53K409165864001.68%
> cutting edge AI architecture advice, something a Fortune 500 CTO would pay $10,000 for > posted for free on X > 2 likes You need to stop following the big accounts and start following ones like this2d ago34K31731113331.94%
The biggest AI workflows, ripe for agent-ification in each department, that we've seen at Varick: 1. Finance a. Manage AP exceptions: most AP teams are managing 600-800 exceptions a month where invoices don't match POs or receipts and require manual resolution b. Automate your month end close: background execution of bank recs, accruals, intercompany, etc. can get your close cycle from 18-22 days to 7-9 days c. Cash application and collections: automate matching remittance advice to open invoices and getting customers to pay on time 2. Sales Ops a. Manage CRM data and rep admin: we see sales reps spend 38% of their time updating Salesforce b. Deal desk approvals: we see deals take 23 days to get discounts, pricing, etc. approved c. Prepare for forecast calls: ensure all the right data is available for each deal to prepare for Monday forecast calls (call notes, stage changes, next steps, etc.) 3. HR a. Manage payroll changes: compare your ATS, HRIS, and payroll systems before payroll to make sure all new hires, raises, terminations, etc. are accurate. Most payroll corrections are due to issues before the payroll run. b. Handle employee inquiries: we've seen up to 80% of inquiries for PTO, benefits, policy questions, etc. can be handled without human intervention c. Onboarding: manage the process from offer acceptance to day 1, including background checks, equipment, account provisioning, I-9, etc. 4. Legal a. Redline contracts: reduce turnaround time from 5+ days to same day for first pass redlines on NDAs, MSAs, vendor contracts, etc. based on your contract playbook b. Contract intake: manage the intake process for contracts, including determining the requestor, type of contract, contract template, contract lawyer, etc. c. Track contract obligations: extract contract renewal dates, notice periods, auto-renewal provisions, etc. from signed contracts 5. Operations a. Disruption management: move from a week-long process to manage shipment delays, supplier issues, etc. to a few hours b. Order management exceptions: many orders have exceptions that require manual management, whether it's a customer on credit hold, bad ship address, lack of inventory, etc. 6. Procurement a. Supplier onboarding: reduce the time it takes to onboard new suppliers from 26 days to 4 by automating collection of W-9s, banking info, insurance, sanctions, etc. b. 3-way match: compare invoices to POs and receiving documents to identify instances of duplicate invoices, invoices over PO price, etc. 7. IT a. Joiners, movers, and leavers: ensure employees have all the right access on day 1, instead of day 4, and remove access the day they leave b. Ticket routing: we see 1 in 3 IT tickets are routed to the wrong team and require rerouting 8. Marketing a. Speed to lead: enrich, score, and route inbound demo requests to the right sales rep in under 5 minutes b. Media reconciliation: reconcile media invoices to insertion orders and media actually run to identify the 8-12% of media spend that is lost 9. Customer Operations a. Route escalations to the right team the first time, instead of having them bounce back and forth between support and engineering multiple times b. Create case summaries to ensure your engineering team doesn't send cases back for additional details All of these can be run in the background using your existing systems (NetSuite, Salesforce, SAP, ServiceNow, Dynamics, etc.) and exceptions can be routed to your people. If you can solve these for your clients, you'll make them very happy. But in order to succeed across the entire company, you have to go about this systematically. Read more on how to go about it below:3d ago32K439404731.1K4.96%
C Suite executives around America are eagerly awaiting Opus 6 and GPT 7 so that they may finally one-shot the agent-ification of their company. By then it will be too late. A fascinating analysis by Daniel, former COO of Citadel Securities EMEA.frozen6d ago29K240171133872.27%
Fantastic read. Everyone talks about AI changing the way we do things, and this is a clear explanation on how AI changes hiring. It's from both sides - how to find, interview, and hire, but also how to be interviewed. Must read.frozen16d ago28K28317805353.00%
The most common reasons why AI pilots fail, even at the 'best' companies: In order of prevalence: 1. Not starting with a thorough process mapping exercise (standard operating procedures vs. what’s actually happening across systems) 2. Automating a process step instead of the entire process (saving 8 mins in a 22-day process still leaves you with a 22-day process) 3. Not capturing baseline metrics for cycle time, cost per transaction, error rate, etc. 4. Building a sidekick instead of a background agent (chatbots will always only be used by ~10% of users) 5. Not embedding the AI into the organization’s existing systems of record (if users have to login, adoption will be low) 6. Not assigning an AI owner (IT built it, the business tolerated it, and no one was “on call” when it broke) 7. Not engaging legal, infosec, and finance early (we routinely see pilots “discover” these groups in month 3 - and get immediately killed) 8. Not scoping for exceptions (the “demo” only covers the “happy path” - but exceptions often represent 40%+ of process volume) 9. Not running the AI in “shadow mode” for a period of time to build confidence (going straight to production often leads to an AI mishap in front of the CFO) 10. Building with only a single AI model provider (one price hike or deprecation later, the pilot is a migration project) 11. Focusing on vanity metrics (e.g. # of logins, “adoption”) instead of business metrics (time saved, $ generated, risk reduced) 12. Not planning for month 7 (who will own and pay for the AI?) 13. Not engaging the subject matter experts (if you capture all their knowledge in an AI, what incentive do they have to help?) Varick does enterprise AI the right way. Work with us below:frozen16d ago28K411316967234.46%
World's greatest vibe coder award https://t.co/ndnujJPgQvfrozen8d ago28K6122140362.41%
Happy Friday. We're hiring Forward Deployed Engineers at Varick. You'll be deployed inside some of the largest companies on Earth, solving their hardest problems and redesigning how the whole business runs so it's AI-native. You won't get bored, because every 3-6 months there's a new company and a new problem waiting for you. Requirements for the role: - Strong engineering. You can write code, design systems, and estimate timelines and resources accurately. - Strong consulting. You communicate clearly, present well, and can command a room of C-suite executives at Fortune 100 companies. - Hungry. We're a lean team of the sharpest people in applied AI, and we're here to win. Apply through our website and we'll read every single application. If it's a fit, we'll reach out by email.frozen20d ago23K359173102642.96%
Top 25 questions the world's largest C-Suite executives ask us re: AI transformation in the enterprise: 1. Are AI transformation firms more like McKinsey or Palantir? 2. We launched Copilot for everyone to use, but why hasn't it changed anyone's week? 3. We've done training, custom GPTs, enablement. What's next? 4. Where do we even start with AI? 5. How do we choose the first workflow to AI-enable? 6. Should AI be owned by IT or the business? 7. Do we really have to train all 10,000 people in the company? 8. How do we enable everyone to build agents, without ending up with agent sprawl? 9. Why are our token costs increasing? 10. How do we know if using AI is really cheaper than just hiring another person? 11. Which steps should remain human? 12. How do we communicate AI won't replace our team's jobs? (Will it?) 13. Will we own the agents you build for us? 14. What does maintenance look like in 6 months? 15. Will our data leave our environment? 16. What models should we be using? What do we do when they're deprecated? 17. Can we run these agents inside our ERP, or will people have to use a new tool? 18. Do we need to clean up our data first? 19. How long until we start seeing ROI? 20. When you talk about ROI, do you mean headcount? 21. How do we keep growing our business without increasing headcount? 22. Why did our last pilot project die in POC purgatory? 23. Our process documentation doesn't match what our team actually does – how do you know what to automate? 24. How do we keep up with how quickly AI is changing? 25. If AGI becomes a reality tomorrow, where should we point it? We solve these every day at Varick. Work with us below:frozen20d ago21K320223166434.79%
The number one question we get from clients is how do they turn a simple skill (brittle, doesn't adapt, isn't that useful) into a full fledged agent. More on that below from our COO.frozen16d ago21K18411804152.94%
Whop? The frontier intelligence company? Nutty.frozen22d ago19K79030310.60%
DeployCo Ode Meta Enterprise Varickfrozen24d ago17K59010410.58%
Everyone's been saying software is solved, robotics is next. Robotics is getting there... this demo is insane.frozen17d ago17K1426201171.55%
We spent the day with ~20 CHROs and Chief People Officers from some of the largest companies in the world (collectively responsible for millions of employees) in Napa this week. Here’s what they asked, what we told them, and where they are in their adoption of AI. 1. Do we need to train everyone on how to use AI? Is that even HR’s job? Partly. Most companies are a barbell in terms of AI adoption. Roughly 5-10% of employees are using it already (on their own). Most aren’t using it at all. Focus on empowering the first group and running a short training program for the second group. Even spending 1 day, or 1 hour per day for 1-2 weeks on how to prompt effectively is extremely valuable. Create a leaderboard for the skill files that save people the most time, but don’t create one for tokens used. 2. Should we let everyone build agents? How do we prevent sprawl? Let everyone build agents, but do it all on the same layer with the same context, and owned by the same person. If you let 400 people build agents in 400 different places, you’re going to have a) tech debt, b) a crazy token bill, and c) 6 agents all trying to do the same thing. Let everyone use sidekicks (Microsoft Copilot, ChatGPT, Claude, etc) to do their work, but have a small group of people take the work off of everyone’s plate with agents that work in the background, built on top of the systems of record. 3. How do we re-engineer processes in HR? The first step is to map out how the process actually works - not how it’s supposed to work. For example, when a new employee is onboarded, the date they start in the ATS may be different than the date they start in the HR system. This almost always results in an email from someone in HR ops to the hiring manager to confirm the start date, which then almost always results in a late order of the laptop, which then results in the new employee not having access on day 1. And none of that is documented in the process. Once the process is mapped out, a process that has 15 steps can be reduced to 5, and 4 of those 5 steps can be handled by agents. This works for anything: payroll corrections, benefits, leave requests, employee tickets, etc. We’ve seen payroll corrections drop by 90% this way. 4. How do we make people feel like AI isn’t going to take their job? Be honest about what changes. Agents take the 90% of the work that repeats, and people keep the 10% that needs judgement, which is usually the part they got into HR for. One of the best ways to sell the implementation is to show the teams what their job will look like when the agent is live. This is another reason to include key stakeholders, who are currently running the process, from day 1. If the plan is to capture all of their tribal knowledge into the agent, they need to see something in it for themselves. 5. What steps of the process should be human-led vs. fully agentic? This decision comes down to two key factors: risk and cycle time. If a step in the process is low risk (think answering a question based on an HR handbook) but high volume, it makes sense for the process to be fully agentic. On the other hand, if the step is high risk (terminations, investigations, comp exceptions, etc.), it should be led by a human, though there may be opportunities for agents to help with the prep work. Most steps, however, fall in between these two extremes and should be built as agents propose + human approves, with full traceability and reversibility. 6. Who on our team should be our forward deployed engineers? Forward deployed engineers should be big picture thinkers who aren’t stuck in the ways things have been done at your company for the past 10 years. They should be knowledgeable on AI and at least a little bit technical. In an HR department, these people often are your HRBPs or your HR ops-minded folks who have already automated much of their job through spreadsheets and macros. Give these folks real problems to solve. You don’t need them to become certified. 7. How can we keep up with the latest in AI? You can’t, really. It changes too fast. Subscribe to some newsletters, but know that most things break on X before anywhere else. That being said, you can get 99% of the value from AI agents with models that are a year old. You don’t need to have the shiniest, most up-to-date model to build valuable agents for your HR team. Key takeaways from the session: 1. Nobody in the room was asking if they should be using AI. Every company in the room had rolled out a copilot or ChatGPT to the whole company, so the question was more around why it hadn’t changed anyone’s week. 2. HR is being tasked with training the rest of the company on AI, often without adequate budget or authority, so the first question is often “Is this even our job?” 3. Despite #2, HR’s own jobs are often the last to be tackled, as teams are focused on rolling out AI to the rest of the company while still running their own processes (even simple ones like onboarding) through email and spreadsheets. 4. There is fear on both sides: employees are worried about how AI will impact their jobs, and CHROs are worried about what they’ve empowered their teams to build. 5. Most people in the room thought they needed to keep up with the latest in AI. They don’t. We solve these every day at Varick. Work with us below:frozen17d ago13K163143512143.18%
My fault guysfrozen10d ago13K219450332.04%
You have to be unemployed in order to keep up with the latest AI advancementsfrozen22d ago13K2448386142.46%
SF is crazy because it’s like paying for New York but it’s SFfrozen29d ago12K17409071.62%
"We are spending millions sending AI experts to learn business. We should be sending business operators to learn AI. Business knowledge is slow. It took most of my career to instinctively know which trade-offs to make around working capital and inventory. AI execution is fast. An experienced operational team can master agentic tooling, evals, and model orchestration in a matter of months. We are running the harder transfer at immense cost and mistaking it for progress." - Praveen Jonnala, CIO of Vistance Networks I can back this up from every implementation we've done at Varick (Finance, Operations, HR, all of it). The exceptions often live in one person's head. Think the vendor who keeps invoicing against a PO that was closed months ago, or the customer whose payment terms got changed on a phone call and no one updated the ERP. These nuances are never documented, and it's not something you can pick up during a 6-week engagement as an AI engineer. On the flip side, we've seen controllers and Accounts Payable managers learn to read evals and write rules that improve agent outputs in just a few short weeks. As a result, when we come into a company to implement AI, we start with the operators of a workflow. Our engineers sit with them to understand every nook and cranny of the workflow, translate that into rules the agents can use, and then train those operators to manage the agents once we leave. If you're interested in doing this kind of AI transformation inside your company, book a call below.frozen6d ago11K143104411112.72%
What has to go right for AI to actually show up as positive in your P&L: - Having the right sponsors: someone from the C suite, with a budget that they can spend and, very importantly, the involvement of the owners of the processes that you're going to be automating. And, also, the early involvement of key stakeholders, like legal, information security, IT, and finance. - Really understanding the as-is processes that you're going to be automating: this requires process mining of the systems of record, and an understanding of the detail of each process, who's involved, all of the variations, and the volumes and the cycle times. - Understanding the baseline of the process and the metric that you're going to measure to understand how much value you're creating: are you looking at time saved, cost saved, revenue increased after spending the tokens, or risk reduced, and agreeing the key KPIs that you're going to measure. - Re-engineering the processes, as you automate them. Usually, you'll end up with about five steps, of which four of them will be automated using AI. And use plain code wherever plain code works. - Building AI agents that work with all of the systems that you've already got in place, like Salesforce, NetSuite, SAP, ServiceNow, Dynamics, et cetera, so that you don't have to move to a new system, or have people log into a new system. And having one architecture layer underneath all of those AI agents. - Deciding which decisions that the AI agent can make versus where you need human input, and making that decision based on both the risk and the cycle time to make the decision. The agent proposes, a person makes the call, and every action is reversible and traceable. - Running the AI agents in shadow mode, in live with your volumes, and comparing their output to the people doing the process now. And not paying out anything they produce until you're absolutely sure that the AI agents are doing what they're supposed to be doing. - Identifying the 5% to 10% of your people who are already using AI, and getting them to be the AI ambassadors and evangelists, and then training all of your people on how they work with the AI agents, and explaining how their jobs will change. - Having a plan for 7 months from now, where you own all of the AI agents that have been developed, all of the data, all of the evaluations, all of the logs, they're all in your cloud, and they're built to be agnostic to any particular AI model. We do all of these at Varick. Partner with us below:3d ago11K156102612073.65%
My interview with Celonis on the state of Applied AI in the Enterprise, below. Thanks for having me on, @billdetwiler!frozen21d ago10K561250841.56%
Super random but my buddy runs a very successful Pokemon card shop. Anyone at Coinbase (or related, and on the BD side) open to chatting? Feels like a good fit. Much appreciated.frozen22d ago9.8K2521040.33%
"AI and jobs is a complex equation". No kidding. Great prescriptive analysis for enterprises, based on real client engagements.frozen6d ago9.7K7810401202.19%
If you're a college student looking to be an HR intern for a few weeks (potential to go full-time), send me a DM/email. Must be based in the US, remote ok. Need you to help us hire our next 50 people.frozen19d ago8.7K47690120.85%
This is my quant My quantitative1d ago8.4K67020221.09%
The only intelligence that matters is knowing what to ask and knowing how to judge the answer, increasingly so in the age of AI.frozen6d ago7.3K1363200312.62%
There's a lot of talk about the big labs and their ability to IPO. This is a must read if you're curious about the AI bubble and how Anthropic vs OpenAI shakes out.frozen21d ago7.1K27731180.79%
soon https://t.co/7PVLD1Ulmafrozen8d ago7.0K95120001.65%
It was never AI vs humans it was always good AI and humans vs bad AI and humans Put those “too dangerous to release” models to good use on the defensive please Godspeed19h ago5.9K6247271.38%
What's better than Apollo for contact enrichment? Feels outdated.17d ago3.2K100141111.11%
https://t.co/wKQ9EmPzVe1d ago3.2K4603011.55%