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One of the pieces of advice we give our clients is to turn high-performing technical articles into technical videos. A strong technical article has already done a lot of the heavy lifting. It has validated demand, attracted the right audience, and proven that developers care about the topic. But, not every article should become a video. We usually look for three things before making that recommendation. First, the topic involves setup, configuration, or implementation steps that are easier to follow when shown. Second, the topic involves technical decisions and tradeoffs that benefit from explanation alongside the implementation. Third, the outcome is something developers can reproduce on their own after following along. Topics that meet those criteria tend to perform exceptionally well in video because developers can see the process, understand the reasoning, and apply it themselves. This has become even more relevant as YouTube continues to grow as a demand capture channel. Ahrefs recently found that YouTube is now the most-cited domain in Google AI Overviews, and we're starting to see the same trend across some of our client accounts. If a topic has already proven that developers care about it, it is worth asking whether video can help it reach more of them.

Top 50 Kubernetes Interview Questions and Answers roadmap.sh/questions/kube…

Three Kubernetes interview questions worth being able to answer cold. 1. What happens when a pod exceeds its memory limit, and why is the answer different from CPU. 2. Why does every request in Kubernetes go through the API server, and what happens if it goes down. 3. How do you actually debug a pod stuck in CrashLoopBackOff, step by step. @roadmapsh has a guide with 47 more like these, grouped from beginner to advanced. Interesting right? Head to the comments to get a head start on your next interview.

In 'Everything Outside Code' Podcast, we talk to a founder, marketing expert or content creator about sales, marketing, operations, growth, and all the other hard parts technical folks suck at. Follow us on YT: youtube.com/@hackmamba

If someone asked you to describe developer relations in one word, what would you say? @stmcallister, a decade-long developer relations practitioner and co-author of Developer Relations Activity Patterns, has a single-word answer: teaching. Not community. Not marketing. Not advocacy. Teaching. In tomorrow's episode of Everything Outside Code, @iChuloo sits down with Scott to talk about how to build DevRel programs that actually move product, what most companies get wrong when they hire for the role, and how to take a technical product that nobody knows about and build a community of champions around it. Dropping tomorrow on YouTube. Link in the comments.

Testing Authentication with Playwright: The Complete Guide currents.dev/posts/testing-…

There is a specific moment in the life of a Playwright suite when auth starts to get a bit complicated. It usually happens somewhere between 50 and 100 tests. Here’s how: - The shared test user starts causing race conditions in parallel runs. - A token expires mid-pipeline and takes down half the suite. - A storageState file gets committed to the repo. - The login UI changes and takes out the helper that every test depends on. The guide by @currents_dev covers how to build authentication testing that holds up past that point. From the credential anti-patterns teams hit first, through OAuth mocking, magic links, TOTP-based MFA, multi-tenant SSO, and the observability setup that makes auth failures debuggable. Full guide is in the comments.

Human-in-the-Loop Testing: Best Practices to Combine AI and Human QA currents.dev/posts/best-pra…

The table below is one of the easiest ways to think about where AI fits in a QA workflow. AI handles the execution-heavy work, like running tests, clustering failures, and identifying flaky patterns and a human reviews what comes back. But decisions about release-critical focus areas, what counts as a real bug versus an environment issue, and whether an AI-suggested fix actually makes sense stay entirely with the human. Final quality and release decisions always do. This @currents_dev article covers the eight best practices that make this division of responsibility work in real teams. Article link below.

Every month before our client review, the full report is already waiting in our inbox. This is how we do it using @GoMarble_AI (We are not their affiliate partners) Step 1: Connect your data sources via Google Auth. GA4, Google Ads, LinkedIn Ads, Google Search Console, Meta Ads, Google Sheets, @HubSpot, @klaviyo, and other tools upon request. Step 2: Write your prompt. For example: "Across all channels this month, show me what drove the most signups, which channels dropped in performance and why, and where we should put more budget next month." Step 3: The report generates in seconds. Channel-level performance, the reasons behind the drops, and a clear view of where to move the budget next. x Step 4: Schedule it to land in your inbox on the 1st of every month automatically. This helps us move faster on what is working and gives our clients clear direction every month.

Join our Hackmamba Creators' Discord Community! hackmamba.io/community/ To join, you must fill out the short form on the right side of the page. Enter your name and other details needed. You can scroll down a bit and see why you should join our other creators + you can check our past community events! 🙌

Week 5 of the Hackmamba API Documentation Sprint is done, and yes, it was one of the most technical weeks yet. @alison_combes documented the REST Countries API in YAML, hit a known $ref bug in Swagger Editor, debugged it methodically by checking indentation, whitespace, and quote types, and eventually traced it back to a version issue with the editor itself. She shared the fix with the whole community. Vidhya worked through the JSONPlaceholder API on SwaggerHub, pushed through the parts that were unclear, and published her spec with a full YAML file attached. Week 6 is live now. The community is moving into testing API documentation, which is where a lot of writers discover that what they wrote and what works are two different things. If you want to be part of what is happening here, the link to join the Hackmamba Creators community is in the comments.

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This is Dev Tools Jobs. devtooljobs.hackmamba.io/?page=1

Did you know? @akuityio is looking for a Community Manager @floxdevelopment is looking for a DevRel Lead @n8n_io is looking for a Senior Developer Advocate @unity is looking for a Senior, Technical Content Marketing @Quicknode is looking for a Senior Technical Writer @nabucasa is looking for a Technical Writer. And more… Our Dev Tools Jobs board puts them in one place so you can find the right role faster. Find your next job in the link below.

5 Best Python Vector Database Libraries actian.com/blog/developer…

Most Python vector database comparisons stop at benchmarks. The failures that cost teams the most time don't happen at the speed layer. Here's a faster picture of the five libraries: Actian VectorAI DB prioritizes deployment stability across edge, air-gapped, and on-premises environments where API consistency matters more than feature velocity. ChromaDB gets you running in minutes but hits real limits in production. You get Python 3.13 incompatibility, Windows instability above 99 records, and a two-package architecture that silently breaks deployments. Pinecone is fully managed with strong async support, but three major versions in 18 months introduced breaking changes that caught production teams off guard. Qdrant has the strongest local-to-production parity of any open-source option. The same client code that runs against :memory: locally runs unchanged against a managed cloud cluster. Weaviate's v4 client is a significant improvement over v3, adding gRPC support and typed exceptions. But the migration takes weeks, and LangChain support lagged months behind the release. The full comparison covers installation issues, async support, error message quality, and a decision matrix across all five. Check it out!

Here are the channels that are working for our clients right now: LLMs are the biggest shift. Hands down! Our clients are seeing an average of 30% of signups coming from ChatGPT, Perplexity, Claude, and Gemini. But LLMs do not work in isolation. They surface your product because your content exists across dev.to, GitHub, and the broader web. The second channel is what feeds the first. SEO is still essential and remains a must-have for LLMs to cite you accurately. But we are seeing signups from organic search drop relative to LLM-driven signups. The two are connected but the mix is shifting. Reddit is working for some of our clients in ways most teams are not doing. We have accounts with 100+ karma that let us seed genuine discussions around specific developer pain points and get conversations going. We also create and manage developer communities on Reddit to distribute content directly. Reddit threads also count as AEO signals, which helps your content get cited in AI search. Search ads still work if you are in an established category and have the budget for it. In the US, expect to pay $100 to $300 per developer signup on search ads. Retargeting on Meta and LinkedIn is one of the highest ROI plays right now. Developers who have already visited your docs or pricing page are close to deciding. We are seeing cost per signup from retargeting campaigns at $30, which makes it one of the most efficient ways to close the loop on warm audiences. Meta ads work well for low ticket products in the $15 to $50 range. Above that the unit economics stop making sense for most devtool companies. Over 3 years of working in developer marketing, we have identified specific LinkedIn groups and Discord channels that consistently drive distribution. We shared last week how distributing a @coderabbitai article in one of these LinkedIn groups drove thousands of impressions from a single post. Influencer partnerships are still worth it at the right price. One of our clients got 500K impressions on X from a $5K spend on their product launch day. That is pretty cheap for the reach you get with a developer audience. What’s working for you?

Soon after we shared our technical writing process at Hackmamba last week, we got a question from a few technical writing enthusiasts: "What AI tools would you recommend for technical writing?" Here's the stack our technical Writer and developer advocate, @khanasjad21, recommends: • @perplexity_ai for research and finding supporting sources. • @NotebookLM for understanding documentation, API references, and product materials. • @NotionHQ for turning research into content briefs and outlines. • @claudeai for working through technical concepts and identifying gaps before drafting. • @boki_io for reviewing content against documentation and catching technical issues. • @GeminiApp for editing, improving flow, and simplifying complex sections. Each tool serves a specific purpose. The value comes from using them at the right stage of the workflow. That said, human involvement is still critical. Someone still needs to validate claims, test code, verify outputs, challenge assumptions, and ensure the content is accurate and useful to the reader. AI can help you move faster, but the responsibility for quality still belongs to the writer. What tools are part of your technical writing workflow?

Migrating from Selenium to Playwright: The Complete Guide currents.dev/posts/migratin…

Most Selenium-to-Playwright migrations stall somewhere between Phase 1 and Phase 2 and never fully recover. The illustration shows the sequence that works. Teams start before touching any code. They confirm ownership, align QA, dev, and DevOps, and set realistic expectations. - Phase 1 builds the foundation. Installing Playwright and rebuilding auth helpers. - Phase 2 introduces new tests written in Playwright, starting from the highest-impact flows. - Phase 3 migrates what remains and retires everything that doesn't need to follow. The full guide by @currents_dev covers each phase in detail, including the infrastructure decisions, the team dynamics, and where migrations most commonly break down. Article link in the comments.