Friday 29th November’s issue is presented by QA Wolf |
|
|
QA Wolf gets engineering teams to 80% automated end-to-end test coverage, and it helps them ship 5x faster by reducing QA cycles from hours to minutes. |
That’s because they provide unlimited, parallel test runs on their infrastructure that gets you pass/fail results in 3 minutes. Here’s how it works: |
They build and automate tests for every single user flow & API in your app. They spin up separate containers in cloud infra to run thousands of tests in parallel. Pass/fail results hit your GitHub repo, Slack, & CI pipeline in 3mins. They maintain and update all tests as your product changes, and they verify all bug reports – so you never see the flakes.
|
|
|
|
— James Stanier |
|
tl;dr: “I’ll pitch the takeaway up front, and it’s this: hold yourself accountable for making decisions and progressing discussions as quickly as possible, by whatever means necessary. Be restless while a decision hasn’t been made. Dead time is your enemy. Be creative about ways of shaving minutes, hours and days from a decision point.” James gives several examples of how to approach this. |
Leadership Management |
|
|
— Will Larson |
tl;dr: In this 20 minute video presentation, Will discusses the under-defined and ambiguous role of Principal Engineers. He defines them as engineers who solve ambiguous, company-wide problems that would otherwise block engineering executives. |
Leadership Management Video |
|
|
— Nishant Shukla |
|
tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now. |
Promoted by QA Wolf |
Management Data |
|
|
— Charlie Andrews |
tl;dr: (1) Advice: imagine you’re giving advice to someone else in your position. What are the concrete next steps you’d recommend they take? (2) Commit: identify how long each day you feel comfortable taking your own advice. I usually find 30 minutes or an hour is good. Some activities also have a natural “increment” that you can use as your commitment, like “I will send one cold email to a potential customer every day.” (3) Exit ramp: give yourself an exit ramp by identifying a date when you’ll reevaluate your commitment. |
CareerAdvice |
“Just believe in yourself. Even if you don’t, pretend that you do and, at some point, you will.” | | | | — Venus Williams |
|
|
|
— Gergely Orosz |
|
tl;dr: “This data is likely to be biased towards early tech adopters and non-enterprise users, as I posted on social media, and self-selecting software engineers active on those sites who are likely to be up-to-date on new tools, and willing to adopt them. There were more replies from developers at smaller companies like startups or smaller scaleups, and very few respondents from larger companies.” |
DevTools |
|
|
|
tl;dr: Built atop OIDC, EASIE works through Sign in with Google (and Microsoft) and includes just-in-time provisioning and automatic deprovisioning. EASIE requires Clerk's Enhanced Authentication add-on (+$100/month) but they've eliminated SSO connection fees, allowing you to add as many connections as you'd like. Click to learn more. |
Promoted by Clerk |
UsefulTool |
|
|
— Angela Ambroz, Alec Brevé |
|
tl;dr: “Sometimes, you just can’t randomize - it’s either not possible, or it’s unethical, or you sacrifice too much precision. In those cases, you can release your treatment to one group and create a composite, synthetic control made up of a weighted combination of your untreated groups.” |
DataScience Product |
|
|
— Sam Rose |
|
tl;dr: Latency visualization shows critical timings for programmers. Operations are displayed as bars with heights proportional to their execution time. |
Latency |
|
|
— John Nunemaker |
|
tl;dr: John discovered his Postgres database was using 87% disk space, mainly due to unprocessed downloads in a podcast hosting app. Rather than batch-deleting millions of old records, they used a table-swapping technique to create a new table with only recent data, freeing up significant space quickly and efficiently. |
AI PostgreSQL |
|
Most Popular From Last Issue |
Engineering Principles – The 5 Step Process — Christian Scheb |
|
Notable Links |
Aisuite: Unified interface to multiple Generative AI providers. |
Multi-Agent Orchestrator: Managing multiple AI agents. |
Payload: Build a modern backend + admin UI. |
Screenpipe: Your AI assistant that has all the context. |
WeSQL: MySQL with compute-storage separation architecture. |
|
|
How did you like this issue of Pointer? 1 = Didn't enjoy it all // 5 = Really enjoyed it | | 1 | 2 | 3 | 4 | 5 |
|
|
