The Journal
The notes I would have wanted at fifteen.
Long-form notes on shipping production systems, scaling SaaS, hiring engineers, AI integration, and the engineering decisions behind Yashveer Labs — written by founder Yashveer Singh.
The Internal CRM Build: When It Pays Off
A custom CRM pays off when the business's sales or customer management process is sufficiently differentiated that off the shelf CRMs require extensive customization that approaches or exceeds the cost of a custom build, when the data model of commercial CRMs does not fit the domain (common in industries with non standard customer relationship models), or when the CRM must be deeply integrated with other internal systems that commercial CRMs cannot connect to efficiently. In most cases, a commercial CRM is the right choice. Custom builds are justified by specific, concrete requirements, not by general preferences for more control.
Business Automation and OpsThe Finance Stack for a SaaS Business
The finance stack for a SaaS business is the set of tools that handle subscription billing, revenue recognition, tax compliance, financial reporting, and payment reconciliation. Most early stage SaaS founders underinvest in the finance stack until the complexity of their billing arrangements, geographic expansion, or investor due diligence forces a reckoning. The right stack, set up early, automates most of the compliance overhead that otherwise consumes founder time.
Business Automation and OpsThe Founder Dashboard: Metrics That Matter
The founder dashboard is a weekly summary of the metrics that tell you whether the business is growing in a sustainable direction. Most founder dashboards measure the wrong things: total users, total revenue, page views, numbers that feel good but do not reveal the health of the underlying business. The metrics that matter are: MRR growth rate, net revenue retention, customer acquisition cost by channel, payback period, active user rate, support ticket volume per customer, deployment frequency, and infrastructure cost per customer.
Business Automation and OpsThe Customer Onboarding Automation Map
Customer onboarding automation is the set of triggered actions, emails, and product flows that guide a new user from signup to their first meaningful use of the product. Done well, it reduces the time it takes users to activate and scales customer success without proportional headcount. Done badly, it sends irrelevant emails to the wrong segment and trains users to ignore your communications.
Business Automation and OpsWorkflow Automation for SaaS: A Founder's Guide
Workflow automation for SaaS is the practice of replacing repetitive manual steps with triggered processes that run without a human in the loop. Done well, it cuts the founder's operational overhead significantly, reduces errors, and gives the team consistent process regardless of who is on shift. Done poorly, it creates fragile pipelines that fail silently and cost more to debug than they ever saved in labor.
Business Automation and OpsZapier vs Make vs n8n vs Custom in 2026
Zapier, Make, n8n, and custom code are four different answers to the same question: how do I connect tools without writing a lot of plumbing? Each one has a different price ceiling, complexity floor, and maintenance cost. Choosing wrong costs more than choosing late. I have run all four in production and the choice almost always comes down to volume, reversibility, and how much the founder values owning the logic.
Business Automation and OpsThe Internal Tooling Build vs Buy Question
The internal tooling build vs. buy decision follows the same logic as the product build vs. buy decision: buy when a commercial product meets the requirements, build when it does not. The failure mode that is specific to internal tooling is the underestimation of the ongoing maintenance cost. Internal tools are often built quickly without tests, documentation, or a clear owner, and then break unexpectedly when the employee who built them leaves or when the underlying system they depend on changes.
Business Automation and OpsThe Business Automation Map: Where Founders Lose Hours
Founder time is the most expensive resource in an early stage company. Every hour spent on a task that could be automated is an hour not spent on customers, product, or sales. The automation map identifies the targets with the highest return: the repetitive tasks that eat 5 to 10 hours per week and can be automated in 1 to 3 days of engineering work.
Startup Failure Postmortems and FearThe Founder Who Tried to Hire AI Out of a Hole
The founder who tries to hire AI out of a hole is using AI tools to accelerate a business that has an unresolved problem at its core. The pattern: the product is not working, customers are not using it, or the team is underperforming, and the founder responds by adding AI features or AI assisted workflows that avoid the hard conversation. AI amplifies speed, not judgment. A business moving in the wrong direction moves in the wrong direction faster when it adopts AI tools.
Startup Failure Postmortems and FearThe Engineer Who Left a Year of Bug Fixes Behind
When an engineer leaves a startup, the visible loss is the salary line. The invisible loss is the year of bug fixes they never documented, the workarounds they kept in their head, and the fragile systems only they knew how to restart. This is a postmortem on that invisible damage, and what teams can do before the resignation email arrives.
Startup Failure Postmortems and FearThe Vendor Outage That Tested Your Disaster Plan
A vendor outage is when a third party service your product depends on fails, and your disaster recovery plan is tested for the first time. Most startups discover their disaster plan is a document nobody read, a Slack message nobody acted on, and a runbook that was last updated when the vendor was first integrated. The postmortem almost always says the same thing: we had no fallback.
Startup Failure Postmortems and FearThe Customer Data Leak That Tested Everything
A customer data leak tests every system in the company simultaneously: the security posture, the incident response process, the customer communication playbook, and the legal readiness. Companies that survive them have all four systems in reasonable shape before the incident. Companies that lose customers, investors, or the business have usually built none of them.
Startup Failure Postmortems and FearThe Wrong Tech Stack Decision That Compounded for Three Years
A wrong tech stack decision is a choice made at the beginning of a product's life that fits the team's current knowledge, the current problem, and the current scale, but turns out to be misaligned with the direction the product takes. The compounding is the part founders do not anticipate. Every new feature is harder. Every new hire takes longer to onboard. Every performance problem is more expensive to fix than it would have been with a different choice.
Startup Failure Postmortems and FearThe Hire That Almost Broke the Team
The hire that almost broke the team is a recurring pattern in engineering organizations: a technically strong candidate who interviews well but has a collaboration style that is damaging to team culture. The damage is typically gradual: dismissiveness in code reviews, credit claiming behavior, unwillingness to mentor, and implicit undermining of other engineers. By the time it is visible enough to act on, the team's trust and psychological safety have been significantly degraded.
Startup Failure Postmortems and FearThe Side Project That Became the Main Project (and the Reverse)
Side projects that become main projects, and main projects that fade into side ones, follow the same underlying pattern: the team did not notice the signal early enough, and by the time they did, the transition cost was high. The lesson is not which path is right. It is that paying attention to where the energy and the customers actually are beats clinging to the plan.
Startup Failure Postmortems and FearThe Founder Coding Habit That Killed Velocity
Founders who code in their own product after hiring a development team create a specific category of velocity problems: unreviewed changes that break conventions, direct production deployments that bypass the development process, and context switching that pulls engineers away from planned work. The pattern is well intentioned (the founder is technically capable and wants to contribute) but the damage to engineering velocity and team morale consistently exceeds the value of the contribution.
Startup Failure Postmortems and FearThe Open Source Maintainer Who Burned Out
Open source maintainer burnout follows a specific and observable arc: genuine enthusiasm, growing adoption, increasing demand, insufficient recognition, and then a sudden or gradual withdrawal that leaves the project in an uncertain state. I have watched this pattern play out up close, and the community dynamics that accelerate it are worth understanding before you depend on a project whose maintainer is already showing the signs.
Startup Failure Postmortems and FearThe Acquisition That Was Worse Than Death
Not every acquisition is a success story. Some are destruction in slow motion, where the acquiring company buys your team, your codebase, and your customers, then dismantles all three. The postmortem is usually the same: culture clash, tech stack replacement, and talent drain. The founders cash out. The engineers leave. The product dies on a roadmap that was approved before the ink dried.
Startup Failure Postmortems and FearThe Layoff That Ended the Founder Engineer Relationship
The layoff that ends the founder engineer relationship is a specific failure mode where the handling of a necessary layoff (the communication, the timing, the selection criteria, the support offered) causes the remaining engineers to lose trust in leadership. The layoff itself is not the failure; early stage companies lay people off when the business requires it. The failure is in the execution: the engineer who is let go without warning or explanation, the team that finds out through rumor, the severance that is not offered, the founder who disappears rather than addressing the impact.
Startup Failure Postmortems and FearThe Reorg That Broke the Engineering Org
A reorg is not a solution. It is a hypothesis that a different reporting structure will produce different outcomes. Most engineering reorgs I have seen failed because they changed the org chart without changing the underlying conditions that produced the original problem. The structure shifted. The communication patterns did not. The output stayed the same, but now nobody was sure who owned what.
Startup Failure Postmortems and FearThe Departure That Took Half the Knowledge With It
When a key engineer leaves a startup, they often take with them the understanding of why the system works the way it does, where the subtle failure modes are, and which parts of the codebase require special handling that is not written down anywhere. This undocumented knowledge is called institutional knowledge, and its sudden absence is one of the most disruptive events in a small engineering team's life.
Startup Failure Postmortems and FearThe Engineer Who Wrote Off the Codebase: A Postmortem
At some point, almost every senior engineer who inherits a messy codebase reaches a moment of private verdict: this cannot be saved. Sometimes they are right. More often, they are experiencing a real architectural problem through a filter of frustration, and the writeoff instinct is a coping mechanism as much as a technical assessment. The postmortem is about what happens when that verdict gets acted on.
Startup Failure Postmortems and FearThe Refactor That Never Ended
An infinite refactor is not a code quality problem. It is a scope problem wearing code quality as a costume. I have seen teams spend an entire quarter on a refactor that delivered nothing to users because no one defined what done looked like before the first commit. The codebase gets cleaner and the product gets slower, and by the end both the engineers and the founders are frustrated at each other.
Startup Failure Postmortems and FearThe Performance Regression That Lost the Largest Customer
A performance regression that loses a large customer almost never happens in isolation. It is the product of a CI pipeline that only checks correctness, a monitoring setup that alerts on errors not latency, and a customer whose data size or usage patterns are meaningfully different from the rest of the user base. The regression was usually introduced weeks before the customer noticed it, and the detection failure is where the real postmortem begins.