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Today’s Docket |
News Stories:
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Thinking about hiring globally? Start with an EOR. |
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The best person for your next role might not live near your office—or even in the same country. |
More companies are realizing they don't need to open entities everywhere just to access global talent. Instead, they're using EOR to hire internationally faster, stay compliant, and avoid building local infrastructure before they're ready. |
Oyster's EOR helps companies hire, pay, and support employees in 180+ countries while Oyster handles payroll, compliance, taxes, and local employment requirements. |
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Latest News from the World of Business |
(1) Indian AI coding startup Emergent hits unicorn status with $130M Series C Emergent raised $130 million in a Series C led by Creaegis, reaching a $1.5 billion post-money valuation — a five-fold jump in six months. The startup has 200,000 paying customers including trucking companies, factories, and property managers building internal tools — and hit $120 million in annualized revenue, up 70% in four months. The signal for founders: the AI coding opportunity isn't just in developer tools — it's in giving non-technical businesses an engineering team in a box. → TechCrunch (2) Anthropic and Blackstone launch Ode — a $1.5B bet that the real AI money is in implementation Ode with Anthropic is a $1.5 billion AI implementation company launched as a joint venture with Blackstone, Hellman & Friedman, and Goldman Sachs, built on the thesis that helping enterprises actually deploy AI is the next trillion-dollar category. For founders, the message is direct: the model layer is commoditizing. Value is moving to whoever makes AI work inside real business workflows. → TechCrunch
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There is a category of bug that is uniquely dangerous in startups. Not because it's hard to find or exotic in nature — it isn't. But because it hides perfectly at small scale, feels like normal behavior, and only reveals itself as a catastrophic performance failure at exactly the moment your growth is accelerating and you can least afford it. |
The N+1 query problem is that bug. It lives inside the codebases of most early-stage products. It was written by a competent developer making a perfectly reasonable decision. And at 100 users, it is completely invisible. At 10,000 users, it is why your product feels slow. At 100,000 users, it is why your product goes down. |
If you're building anything with a database — which is to say, if you're building anything — this issue is for you. |
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Your competitor's growth lead already saw the spend spike. |
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While your team is still in standup, the other growth lead already got the alert. Viktor is an AI employee that lives in Slack. It watches your Meta and TikTok spend overnight, flags the underperformer by 7am, and drafts the new brief before your first meeting. |
Get Started for Free |
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What the N+1 Problem Actually Is |
The name comes from the pattern itself. Instead of making 1 database query to fetch a collection of records, your application makes 1 query to get the list, then N additional queries — one for each item in the list — to fetch associated data. One query plus N more. N+1. |
Here's the simplest possible example. You're building a social product. You want to display a feed of 50 posts, each showing the author's name and profile picture. |
Your code fetches the 50 posts: that's 1 query. Then, for each post, it fetches the author's details: that's 50 more queries. Total: 51 database round trips to render a single page. |
Now imagine that feed has 200 posts, or that 5,000 users are loading their feed simultaneously. Suddenly you're making tens of thousands of database queries per second for what should be a simple read operation. Your database connection pool exhausts. Response times spike from 200 milliseconds to 8 seconds. Users see a spinner. Some leave. You get paged. |
And the code that caused it looked completely reasonable when it was written. |
Why It's So Easy to Write and So Hard to Spot |
The N+1 problem is a natural byproduct of how modern ORMs — ActiveRecord in Rails, Eloquent in Laravel, Prisma in Node.js, SQLAlchemy in Python — abstract away database interactions. |
ORMs are genuinely useful. They let you write post.author.name instead of raw SQL. They make data access fast to develop with. But that convenience has a hidden cost: the ORM makes database decisions on your behalf, optimized for developer ergonomics, not query efficiency. |
When you write post.author inside a loop, the ORM doesn't know you're in a loop. It doesn't know 50 other posts need the same data. It just executes a query for that one author, then another for the next post, then another, then another. |
The worst part: in development with small seed datasets, N+1 queries are essentially undetectable. Queries run so fast they don't register as a problem. They only become visible under real production load, with real data volumes. The feedback loop between writing the bug and feeling its consequences can be months long. |
The Fix: Eager Loading |
The solution is conceptually simple. Instead of fetching associated data lazily — on demand, one record at a time — you tell the database upfront what you need, and it fetches everything in as few queries as possible. This is called eager loading, and every serious ORM supports it. |
In Rails: Post.includes(:author).all instead of Post.all |
Instead of 51 queries for 50 posts, you now have 2. A feed of 1,000 posts still takes 2 queries. Database load no longer grows with record count. |
In Django: select_related() for direct relationships, prefetch_related() for many-to-many. In Prisma: include. In SQLAlchemy: joinedload(). The syntax varies, the concept is identical — tell the framework what you need before you start looping, not while you're inside the loop. |
The discipline is simple but demands consistency: whenever you fetch a list of records you'll iterate over, ask yourself what associated data you'll need, and load it upfront. |
Query Visibility: The Real Structural Fix |
Fixing individual N+1 queries matters, but the deeper problem is that most early-stage teams have zero visibility into what their database is actually doing at runtime. |
The ORM abstracts the SQL away. The developer sees clean code. The database silently executes hundreds of queries per request. Nobody knows — until a performance incident forces someone to dig into logs. |
The single highest-leverage change you can make is adding query detection to your development workflow from day one. In Rails, Bullet automatically detects N+1 patterns and alerts you in real time — before code ships. In Django, django-silk does the same. These tools watch your query log during a request cycle and flag when the same pattern executes N times in a loop. They make the invisible visible, which is the only way to systematically prevent a class of bugs that looks innocent in code review. |
In production, you need query-level observability — not just application metrics. Tools like Datadog APM, pganalyze for PostgreSQL, or even basic slow-query logging will show you which queries run most frequently and which code paths generate them. A spike in query volume without a corresponding traffic spike is almost always an N+1 regression that slipped through. |
If you can't see your database queries in production, you don't understand your application's performance. |
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Many people struggle with managing their personal finances effectively. Keeping track of expenses, budgeting, and understanding where money goes can be overwhelming. A startup could develop a data science-driven personal finance platform that analyzes individuals' spending habits, income, and goals to provide customized financial advice and recommendations. By using machine learning algorithms, the platform can offer insights on areas for potential savings, investment opportunities, and help users reach their financial goals more efficiently. This would simplify the process of managing finances and empower individuals to make informed decisions about their money. The potential market for this type of service is substantial, as nearly everyone has some level of interest in improving their financial situation. |
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Disclaimer: The startup ideas shared in this forum are non-rigorously curated and offered for general consideration and discussion only. Individuals utilizing these concepts are encouraged to exercise independent judgment and undertake due diligence per legal and regulatory requirements. It is recommended to consult with legal, financial, and other relevant professionals before proceeding with any business ventures or decisions. |
Sponsored content in this newsletter contains investment opportunity brought to you by our partner ad network. Even though our due-diligence revealed no concerns to us to promote it, we are in no way recommending the investment opportunity to anyone. We are not responsible for any financial losses or damages that may result from the use of the information provided in this newsletter. Readers are solely responsible for their own investment decisions and any consequences that may arise from those decisions. To the fullest extent permitted by law, we shall not be liable for any direct, indirect, incidental, special, or consequential damages, including but not limited to lost profits, lost data, or other intangible losses, arising out of or in connection with the use of the information provided in this newsletter. |
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