Jon Haddad

📣 Today we’re announcing new features for Jellyfish AI Impact that bring together system data, developer sentiment, and guided insights - giving leaders a clearer, faster, and more complete picture of how AI is transforming their organizations and the overall SDLC. As AI becomes embedded in every corner of software development, engineering leaders face a new challenge: proving that these tools actually deliver value. From autonomous agents to code assistants, the landscape is evolving faster than most teams can measure. Keep up with the AI evolution with Jellyfish AI Impact: https://lnkd.in/g7t3wx5h

45

1 Comment

Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

8

1 Comment

Tired of long CI builds and rising compute costs? Coda was too. They trialed Aspect Workflows, saw a 10x improvement in build times and a 67% reduction in compute costs, and decided to roll it out more broadly. "We went from having significant limits in CI and tools to where the limits are now just due to our code." — Neal Norwitz, Software Engineer at Coda Read more about the challenges they faced and the results they saw: https://lnkd.in/eA_pwcDQ

5

If you are building or evaluating adding AI to reliability, this is a must watch. This episode of Slight Reliability breaks down where AI helps in SRE and operations, and where it falls short. Causely founder Shmuel Kliger, explains why modern observability turned operations into a big data problem, and why causal reasoning is required to infer the single upstream cause behind many degraded services. A practical conversation about reducing toil, avoiding AI hype, and focusing on what actually works. Full episode here: https://lnkd.in/epj_x7q6

20

One of the most common concerns I hear when discussing direct reads from Kafka for analytics is the perceived high egress cost. But this often overlooks two important technical realities. First, the data is returned batched and compressed, meaning the actual size “on the wire” can be several orders of magnitude smaller than the raw payload. Second, with Streambased indexing, queries can efficiently target only the relevant records rather than scanning entire topics indiscriminately. Together, these factors dramatically reduce the volume of data moved, and therefore the cost, making this approach far more economical than it might initially seem. What’s especially interesting is that most managed Kafka providers haven’t caught onto this model yet. That means you're not (yet) being penalized with ambiguous charges like "data that could have been processed" (looking at you, BigQuery). These kinds of cost calculations are nuanced and require effort—but they can reveal surprisingly straightforward ways to hit your cost-reduction goals without sacrificing flexibility or fidelity.

8

3 Comments

Kubernetes is powerful. But YAML? Not so much. Here’s Harpoon in 60 seconds: No steep learning curve No YAML No security gaps Just drag, drop & deploy Visit the site & try free demo.

3

🚩Feature Flags: How to Ship Faster, Safer, and Smarter 🚩 Dinesh A., Principal Engineer at Wells Fargo, is breaking down feature flags at Code Remix Summit. Not just what they are, but how to actually use them without creating chaos. Get best practices that prevent flag sprawl and technical debt. Hear real case studies showing how companies are using flags to ship with confidence. 👍 https://lnkd.in/g5r2MUSJ #CodeRemixSummit #FeatureFlags #WellsFargo #ContinuousDelivery #DevOps

50

Every engineering leader says they care about velocity. But velocity doesn’t die in the IDE. It dies in: Review queues Approval loops Dependencies across 3 other teams The fastest teams don’t code faster. They unblock faster.

1

📣 Now GA: Terraform Stacks Managing infrastructure doesn’t have to be complex. With Terraform Stacks, you can use prebuilt workflows to save time, reduce overhead, and scale confidently — whether you’re running a small setup or managing multi-cloud environments. Build at scale with reusable patterns and built-in guardrails. https://bit.ly/4mppkMZ

330

2 Comments

Julia Blase, Product Manager at Chronosphere, discusses how observability needs to evolve alongside Kubernetes. She explains that Kubernetes brought dynamic, scalable infrastructure, and monitoring systems must follow suit. The future requires on-demand instrumentation and dynamic data collection that can scale up and down based on needs, helping teams optimize costs while getting the right insights at the right time. Watch the full interview: https://ku.bz/X6tgWrG0P

2

LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

32

Diskless Kafka is exciting, but the biggest wins might come from a less obvious place: the clients. Default producer configs are silent cost drivers. They inflate traffic, waste resources, and kill efficiency. That’s why we built SuperClient at Superstream - to dynamically tune Kafka clients based on workload. No more static defaults. Just better performance and lower costs. Diskless Kafka? Love it. But let’s not forget the clients. #ApacheKafka #KIP1150 #CloudEfficiency #KafkaOptimization #StreamingInfra

10

Promotion is where delivery pipelines often fail at scale. Custom scripts, manual approvals, and limited visibility introduce risk and slow teams down. 🧩 Kargo is designed to make promotion a first-class GitOps workflow. This free training walks through how to define stages, manage freight, and promote applications safely across environments using GitOps principles. 🔁 The course is a practical starting point for teams running or planning to run promotion workflows alongside Argo CD 🐙, without adding bespoke automation or fragile glue code. 👨‍💻 Enroll for free. https://hubs.li/Q03-jQGW0 #GitOps #Kargo #DevOps #PlatformEngineering #CloudNative

6

🚀 Are your pull requests slowing your team down? Most teams treat every pull request the same. But not every change needs the same level of attention. That’s where Ship / Show / Ask comes in. With DevSensei, you can structure your PR workflow into three simple categories: ✅ Ship For small, low-risk changes. No need to wait, just merge and move on. 👀 Show For changes you want visibility on, but without blocking progress. 💬 Ask For complex work that needs discussion and proper review. This approach helps teams: • Reduce waiting time on trivial PRs • Focus reviews where they actually matter • Keep delivery speed high without sacrificing quality Instead of one rigid process, you get a workflow that adapts to the change itself. Because not every PR should slow you down. 👉 Curious how it works in practice?

3

If you want your DevOps workflow to feel lighter and faster. This video is for you. In this episode, SAHIL VANDRA, Senior DevOps Engineer I, walks through how he uses GitHub to simplify two major parts of his workflow: - Automated code reviews - Smarter CI/CD pipelines He breaks down how AI helps reduce manual effort, speed up releases, and maintain steady deployments without compromising on quality. If you’re looking for practical ways to bring AI into your DevOps routine, this breakdown gives you a clear, real-world look at what actually works.   Watch the full video here: https://lnkd.in/d3zYaxgb #devops #aidevops #mlops #github #automation

26

Cribl Stream helps you control how data flows. Imply Lumi helps you control how data is stored and searched. Together, they deliver a modern observability architecture built for control, performance, and efficiency: - Route and retain what matters most - Search quickly and interactively across all retained data - Integrate seamlessly with your existing stack - Reduce costs without disrupting workflows Lumi isn’t just a faster backend — it’s part of a next-generation architecture that decouples storage, indexing, and query from the observability tools above. That means greater flexibility, less lock-in, and a solid foundation for what comes next. https://bit.ly/49iAXm1

18

2 Comments

This demo is about accelerated polling, a feature allowing all Heroku Connect users to sync data between their Heroku Postgres databases and Salesforce orgs much faster than the default interval polling.

8