There's a version of Linearloop three years from now that looks very different from today.
The people joining now are the reason why.
#Linearloop#TeamLinearloop
Most CRO efforts fail because engineering is missing. Design tweaks can’t fix system-level issues. Real optimisation needs control over the system.
Swipe to see what’s missing →
#cro#engineering
You can increase conversion rate and still hurt revenue.
More signups, more clicks, and more form fills, but nothing compounds downstream.
That's not growth. That's optimising behaviour.
If that feels familiar, check the full breakdown here: https://t.co/IFDjpNOtMn
Running A/B tests isn't the same as running good A/B tests.
Small samples. Early peeks. Tests that run until they "win."
You're not making data-driven decisions. You're making decisions and finding data to justify them.
Fix the methodology.
Most lead forms don't fail because of bad design.
They fail because of bad questions.
If a field doesn't impact routing, qualification, or prioritisation, it shouldn't exist.
Click the link to read the full blog: https://t.co/e2ERg96ECi
Embedded checkout feels like the obvious choice until scale hits, geographies expand, and compliance changes.
What's easy today becomes the bottleneck tomorrow.
Checkout is an architecture decision. Choose wisely.
The highest-intent page on your site is probably the one you're most afraid to touch.
Demo forms are tangled into CRMs, routing logic, and SDR workflows.
So, teams leave it alone. That's the mistake.
Full breakdown here: https://t.co/NNCUyfx7yI
Every unprotected model call is a potential data leak.
Minimal access controls around model inputs, outputs, and data aren't a gap. It's a liability.
→ RBAC
→ Encryption
→ Audit trails
Conversion rate is misleading without funnel-level tracking.
Page-level wins can hide funnel losses.
Attribution gaps + weak event tracking = false A/B insights.
CRO is a system problem.
Most CRO teams pick between UX and personalisation.
That's the wrong question.
Both are just tactics. Without a system connecting behaviour, experimentation, and execution, nothing compounds.
Full breakdown: https://t.co/UDGGW4YjJS
Most outages aren’t caused by bad code. They’re caused by bad deployments.
Your deployment strategy defines reliability.
Swipe the carousel to know the key strategies every DevOps engineer should use.
Last weekend, we didn't hit a milestone. We did something better.
The Linearloop team went to Udaipur. No calls, no sprints, just people who build together, being together.
That's the culture we're building.
Your SaaS product is generating all the data you need.
You're just getting it too late.
Dashboards and weekly exports don't drive decisions. Real-time event pipelines do.
The "10x engineer" is a myth.
Productivity isn't about one brilliant individual. It's about the system they operate in.
Bad architecture + slow pipelines = your best engineers fighting the system
Most AI projects don't fail because the model is bad.
They fail because the data stack wasn't built for production.
A demo and a production AI system are two different things.
Read the full blog here: https://t.co/CeZ0p7rhgP
Kubernetes isn’t always the answer.
It solves real scaling and orchestration problems, but adds operational complexity.
Before migrating from traditional infra, ask: Do you truly need container orchestration at scale?
Swipe the carousel to break it down.
"We use a private LLM. So, we're secure."
No, you're not.
Private ≠ secure. VPC hosting doesn't fix leakage, unauthorized access, or audit failures.
Security is the foundation.
Check the full blog here: https://t.co/Z5xwn3tVxx
Your AI demo worked perfectly. Then real users showed up.
Cold starts, memory crashes, API timeouts.
Production LLMs aren't a model problem. They're an infrastructure problem.
Latency is a product decision.
Most companies pick the wrong AI architecture and realize it too late.
RAG vs Fine-tuning isn't a technical call. It's a capital and risk decision.
Wrong choice = sunk cost.
Check the full breakdown: https://t.co/mKW60l52ot