Hackmamba

Big announcement for job seekers in the Devtool marketing space 👋 We’ve launched the Hackmamba Devtool Jobs portal! We regularly hear from technical writers, DevRel, and growth professionals who struggle to find devtools roles without checking multiple places. Jobs are scattered across communities, LinkedIn, and company career pages, making the search slow and inconsistent. To solve this, we built a dedicated portal that brings relevant devtool roles into one place. Listings are curated from communities, LinkedIn Jobs, and our own network, so you can quickly see what’s open and decide what’s worth exploring. We refresh the listings every Friday. Bookmark it to stay updated on the latest Devtool Jobs 💜

When to Choose On-Premises vs. Cloud for Vector Databases actian.com/blog/databases…

At 500M vectors and 200M monthly queries, a managed cloud vector DB can run you approximately $8,500/month. An equivalent on-prem setup cuts that roughly in half and costs drop further after the hardware payback period. That's just one reason the on-prem vs. cloud conversation for vector databases looks different in 2026 than it did two years ago. The full guide by @actiandev breaks down cost, compliance, latency, and the hybrid middle ground. Link in the comments.

Browse new tech jobs here: devtooljobs.hackmamba.io/?page=1

Looking for a job? We’ve added 23 new job roles to the devtools job board today. If you have time today, make sure to go through them. Happy Workers’ Day! Link in the comments.

When should you use canary deployments? dev.to/coderabbitai/w…

A canary deployment without a plan is just a controlled way to ship a broken release to fewer people. Four things need to be decided before you push: 1. Who gets it: Your cohort should actually use the feature you're shipping. Rolling out a mobile dashboard overhaul to desktop users tells you almost nothing. 2. How long will the test run: Some failures show up in minutes. Others take days to surface, especially anything tied to peak traffic or database behavior under load. 3. What does success look like: Track error rates, memory, CPU, API usage, traffic distribution. Define the threshold for rollback before you start, not during the incident. 4. Do you need feature flags: For teams doing canary deployment regularly, feature flags make cohort management cleaner. The full guide of @coderabbitai walks through each decision point and the risks that still exist even in a well-planned canary release. Read to avoid the mistakes.

Most content teams skip technical review entirely. Or they hand it to an engineer who has 12 other things to do. @hackmamba uses Boki's technical review agent for every piece they produce for clients like @ActianCorp, @mintlify, and @coderabbitai. The agent audits technical terms for accuracy, validates that every claim is backed by a source, checks code behavior, and flags anywhere the piece loses coherence as a technical argument. Then a human content marketer steps in to test the code and evaluate whether the piece would hold up under scrutiny from an experienced developer. Result: 65% less back-and-forth between writers and reviewers. If you are a technical writer, try Boki's technical review agent on your next draft.

@robjama Uhm..

the more unemployed you are, the better you get with AI

@appwrite Thinking how many vibe-coders have been to this ATM?

when claude writes a bad useEffect

We often see teams wait until docs become a bottleneck before acting on it. By then, it’s already affecting support, onboarding, and integrations. Here's a reminder from @BibiTheWriter

.@ActianCorp VectorAI DB landed in the top 5 on @ProductHunt yesterday. 193 upvotes on launch day. Thank you to everyone who showed up and supported. The portable vector database for AI agents beyond the cloud. If you have not tried it yet, the community edition is free. actian.com/databases/vect…

Scenes from yesterday at The AI Developer Conference in San Francisco. @iChuloo ran a workshop with Charlie Wood, Global Principal Architect at Actian, on building a Post-Discharge Copilot using @ActianCorp VectorAI DB. Local-first agentic retrieval for regulated environments. How AI agents can assemble patient context, detect risk signals, and support clinical decision making without sending sensitive data to the cloud. 3,000+ developers. @AndrewYNg hosting. Good room to be in.

Should You Use RAG or Fine-Tune Your LLM? actian.com/blog/databases…

RAG dominates enterprise AI right now, with 51% of deployments using it in production. But that doesn't mean it's always the right call. The actual decision comes down to three variables: how often your knowledge changes, how much query volume you're handling, and what your team can realistically maintain. Fine-tuning wins on latency and structured outputs at scale, RAG wins on flexibility when your data is moving fast. Hybrid systems combining both have shown the best benchmark results across the board. This article by @actiandev breaks down exactly when each approach makes economic sense, including the cost math that most RAG vs. fine-tuning comparisons skip entirely. Check it out below.

Read the full blog here: 9 Strategies to Get the Most Out of Playwright Test Agents currents.dev/posts/9-strate…

Most teams using Playwright test agents treat them as a one-off tool; generate some tests during a sprint, review the output, and move on. The teams getting consistent value are the ones who've embedded agents into how they work every day: using the Planner during local development to map out interaction paths, the Generator during pull request reviews to cover new components, and the Healer to triage CI failures before they pile up. There's also a model selection angle that most teams skip. Stronger LLM models follow your conventions, fixtures, Page Objects, and locator hierarchy, far more reliably than free or low-cost ones. Getting that right early saves a lot of post-generation cleanup. We put together nine strategies covering architecture, review cycles, data state management, agent scope, and more. A few of them reframe how most teams are currently using agents. Full article of @currents_dev is linked in the comments.

Subscribe to Everything Outside Code: @hackmamba" target="_blank" rel="nofollow noopener">youtube.com/@hackmamba

On last week's EOC we talked about agent awareness. How do you get AI agents to know your product exists. This week goes one level deeper. If agents are the new audience, how do you create content they can actually find, read, and use? @AccordionGuy has spent years building developer products and taking them to market. He has a very specific take on how content creation changes over the next 18 months. Dropping later on Youtube (9am CET)

VectorAI DB is now public. The first vector database built for RAG, semantic search, and real-time AI agents on-prem, at the edge, or air-gapped. Come throw your hardest problem at it.

We're excited to announce VectorAI DB, the first vector database purpose-built for high-performance, reliable AI at the edge. RAG isn't dead. It just can't run in the environments that need it most. In manufacturing, 46% of AI pilots never leave the OT network. Healthcare, defense, and financial services are no different. VectorAI DB runs RAG pipelines, semantic search, and real-time AI agents on-premises, at the edge, or air-gapped. Before today, 1,000+ devs across three hackathons had already built on it. A maritime AI system. An on-device AI therapist. Cardiac imaging on a closed hospital cluster. And these are just a handful of what VectorAI DB makes possible. VectorAI DB is NOW OPEN to the public. Come throw your hardest problem at it. Download link in the comments.