6+ months ago, Trackliy was just an idea in my mind.
Since then, the road has been full of personal struggles, financial pressure, sleepless nights, doubts, and moments where giving up felt easier than moving forward.
I know this post may not get many likes.
Maybe no one will care.
Maybe it’s only because I don’t have enough followers yet.
But that doesn’t change what this means.
Behind the silence, behind the low numbers, behind the unseen work… something real was being built every single day.
Today, Trackliy has crossed 2960+ minutes of testing. ✅
Every setback became fuel. Every hard day became part of the foundation.
Soon, your tasks, chats, calls, and team workflow will align in one place.
One day people may notice the result.
But only I will know the story behind it. 🚀
#Trackliy #BuildInPublic #FounderJourney #StartupStory #SaaS #StartupLife #Entrepreneurship #IndieHacker #NeverGiveUp #FromStruggleToSuccess #DreamBig #Resilience #ProductLaunch #TechStartup #Motivation #FutureOfWork #ProjectManagement #Productivity #Hustle #LaunchSoon
I turned 26 today 🐣
A few things I've learned so far:
1. Nobody has it figured out. 2. Quitting isn't always failure. Sometimes it's just making room for the right thing. 3. People called me stupid when I was younger. Turns out I just hadn't found what I was good at. 4. Every skill stacks. Photography got me into UX. UX got me into startups. 5. Trust your gut more. 6. The right people make life way easier. 7. Don't waste your brain on dumb stuff. 8. There is no oversight success. 9. Don’t compare your chapter 1 to someone else's chapter 20. 10. If you want a different life, nobody's gonna build it for you. So take care about it by yourself.
Let's see what 26 has for me
Always so much fun to chat with @3blue1brown
AI has been making much faster progress in math than in other fields.
As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like.
Even within mathematics, there's a jagged landscape. What does it look like?
What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do?
Does AI (on net) increase or decrease human understanding of the field?
How big is the overhang from having AIs systematically try to connect ideas already in the literature?
And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI?
0:00:00 – AI is discovering new proofs. Is that AGI?
0:11:32 – The verification loop on conceptual breakthroughs can be a century long
0:26:12 – Will we understand an AI proof of the Riemann hypothesis?
0:38:08 – Can AI find the hidden bridges between fields?
0:53:48 – Why real-world tasks don’t fit into RL environments
1:07:07 – Good writing requires theory of mind that AI still lacks
1:16:02 – Why learning will still depend on human curation
Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.
Shown is now 100 people across 40 countries…
Problem: We need more cracked ppl. The best in the world.
Solution: I hire you
The roles we need filled right now:
> Creative Director (directing viral launch videos)
> Graphic Designer (making UI/graphics for our videos)
> Motion Designers (animating graphics that get millions of views)
> UGC Engineer (managing an army of 100s of UGC creators)
and soooo many more
Here’s what you’d get:
- You’d work directly with CEOs of billion dollar companies, you get full ownership in your work, and you’ll leave with 100M+ views on your videos.
- You’ll be a part of the biggest tech launches on the planet.
- You’ll do the best and most fun work you’ve ever done.
- If you think you’d be a good fit for us - DM me a one paragraph pitch of what you’re good at. (I’ve hired 3 ppl this way in the past month)
Know someone good? Refer them and I’ll send you up to $5,000 if we hire them.
CONGRATULATIONS, YOUR '$200K PROMPT ENGINEER' RESUME IS NOW COMPLETELY WORTHLESS
The absolute biggest lie in tech just got exposed.
If your entire AI strategy is still just whispering the "perfect prompt" to ChatGPT, you are fighting a losing battle.
The prompt wizard era didn't just slow down, it vanished.
Welcome to the brutal reality of Loop Engineering.
Stop begging an LLM for a single decent answer.
The real masterminds aren't typing; they are building closed-loop, self-correcting AI agent networks that launch, audit, fix their own code, and execute multi-step operations on a continuous, autonomous wheel until the task is flawless.
The $45,000/Month Ghost Agency:
Companies are desperate to replace human overhead with these autonomous loops, but their internal teams have no clue how to build them.
The Play: Architect custom "Agentic Loops" for legacy B2B companies.
The Ticket: Charge a massive $15,000/mo infrastructure retainer per system.
The Scaling: Lock in 3 enterprise clients, deploy the loop architecture once, and sit on a $45k+/mo cash-printing machine while the AI manages itself.
You can either keep tweaking your little prompts for pennies, or you can build the engines that automate the tech industry.
Most companies are implementing AI agents RANDOMLY for non-coding use cases... And it's hurting more than it's helping.
Over the past few months, we've helped 50+ companies implement AI agents for ads, content, operations, analytics negotiations +more. Here are 8 useful workflows that have driven the most tangible value.
1] Ad scraping / Competitive & Market Intelligence
A company called “Foreplay” (weird name) has an API that lets you scrape your competitors' ads, sort them by performance, and download the actual creative assets directly. This is extremely useful for ad teams. Instead of asking an agent to write from scratch, you can have it constantly study the best-performing ads in your market, pull the patterns, and use those examples to generate better hooks, scripts, visuals, and campaign ideas. Putting this agent in Slack where the entire marketing team can access is incredibly useful.
2] Organic Content Scraping
All AI models are bad at writing content scripts, ads, and other forms of media... until you ground them in high-quality examples. When you let an agent research any niche, scrape the best-performing posts across platforms, and understand what's actually working, the output gets dramatically better. This can take real setup time because scraping services are often fickle, but once the data pipeline works, it becomes one of the highest leverage inputs for content/marketing. For those making videos this skill is really useful because agents can download full youtube videos, and cut relevant parts of the videos down and organize them into folders. Video editors LOVE this one for B-Roll and cinematic edits.
3] Content Re-Optimization
Listen to me: AI agents can watch your videos (Gemini has great video analysis abilities) and make precise edits if you give them the right tools. So many marketers make a video, it underperforms, and then they just go make an entirely new video. That is usually the wrong move. A lot of the time, the video does not need to be redone... It needs 1-3 micro-optimizations:
> better / shorter hook
> cleaner cuts
> better first frame visual
> stronger caption
When you set this up properly, an agent can find the weak parts of the video, make the edits, and even re-upload the video or rerun the ad automatically.
4] Make All Meeting Notes ACTUALLY retrievable (With Permissions)
Almost everyone has AI joining meetings to take notes. But very few companies have a system that keeps those notes organized, searchable, and permissioned properly. Anyone on the team should be able to find important context from past conversations in under a minute. You should be able to ask the company memory, “What did we decide about this?” and get the answer instantly, only from the conversations you’re allowed to access. Amazing how many companies just record stuff and people have no idea how to ask a question about that data.
5] Automated HIGH SIGNAL Dashboards
Most companies produce too many summaries, too many digests, and too many random links with too much information. Many companies we've talked to actually get annoyed with too much information. One of the highest-leverage uses of agents is deciding the FEW metrics that actually matter, organizing them into a clean single glance dashboard, and having an agent send a concise daily summary of what changed and what it means. The agent's job should be to decide what actually matters. This can't be achieved without a good amount of upfront work. It's worth it.
6] Lifecycle Email Agents
Tools like @loops and @resend give agents control over sending, sequencing, and personalizing emails. Instead of one static onboarding flow, an agent can manage the full customer lifecycle: welcome emails, activation nudges, abandoned signup follow ups, renewals, win-backs, etc. This one takes time to set up, and I still think it should be done with human in the loop, but it saves SO much time, especially if you configure skills properly.
7] Brand Negotiations
We've been setting up agents for creator agencies that manage large creators on Instagram and TikTok. With Fable level models, agents can handle 95%+ of the negotiation process... collecting campaign details, comparing terms, drafting email replies, flagging risky clauses, and escalating the parts a human should approve. I think agents will handle most of the back-and-forth over email, as long as the human-in-the-loop checkpoints are extremely clear before anything is agreed to or signed.
8] Genmedia
@fal has a “genmedia CLI” that can use any creative model (Image + Video). When you configure it properly, and only route people to the best models, it becomes extremely useful for marketing teams. You can put it directly in Slack so the whole team can generate ad concepts, content assets, website visuals, product images, and creative variations without jumping between a bunch of different external tools.
These are all things we've noticed when working with companies helping them become more @agentnative_. Videos on all of these individual workflows soon!
I built an AI Influencer automation in MakeUGC
... that Automatically Creates UGC Videos while you sleep!
- Perfect pacing
- Cinematic lighting
- Natural human motion
- AI-powered scripting + iteration
Comment “UGC” and I’ll send you the full workflow.
The 2026 Vibe Coding Playbook for Lovable is live.
I've spent 1,000+ hours building with @Lovable and shipped 250+ production apps through my agency.
Here's what I cover ↓
0:00 - Why 90% of Vibe Coders Fail Before They Start
1:15 - The Spec Template That Prevents Rebuilds (Data Model, User Roles, Core Flows)
3:30 - Using Plan Mode to Auto Generate Your Spec
5:00 - UI Shell First (Static Layouts Before Any Logic)
6:45 - The 4 Ingredient Design Prompt (Spec, Stack, Esthetic, Motion)
8:30 - React + Tailwind + Framer Motion Stack Walkthrough
10:00 - Using Screenshots and Linear/Stripe/Vercel as Design References
11:30 - Lovable Cloud Setup (Row Level Security Done Right)
13:00 - Cloud Auth, Profile Rows, and Protecting Logged In Routes
14:30 - Replacing Mock Data With Real Queries Page by Page
16:00 - The Feature Build Template for Anything Post Launch
17:30 - Security Audit, Error Handling, and Performance Prompts
18:45 - Hitting Publish and Connecting a Custom Domain
Bookmark for later & follow me for more builds like this.
not tryna hate on @levelsio, i have upmost respect
but his churn seems to be wild
when i started following him Nomads was $30K MRR, PhotoAI was like $130K MRR, Interior $50K or something..
it doesnt matter cuz hes already escaped the matrix 10x over but jus something i noticed..