|
By Alan Mon
The rise of AI-assisted coding has transformed how software is built. With tools generating entire features in seconds, the bottleneck is no longer writing code—it’s verifying it. Because AI can generate boilerplate and handle API integrations instantly, more service changes are being pushed into authentication logic, API calls, and configurations. Teams desperately need a way to verify these changes before merging, especially when the code touches external dependencies.
|
By Ken Ahrens
Here’s a problem I run into constantly: something breaks in production, I can see the 500 errors in my logs, but I can’t reproduce it locally. The trace shows me the dependency graph but not the actual request that failed. This is especially painful in microservices. I was looking at a CNCF example the other day (a simple demo app, like 4 pods) and it already had so many cross-service dependencies that understanding what broke required looking at the whole system at once.
|
By Ken Ahrens
Everyone wants an AI agent factory in 2026. Autonomous agents fixing bugs and shipping features while you sleep. I’ve been building toward that myself. But the error rates don’t support the fantasy. The best AI coding agents in the world fix about 50% of real bugs on SWE-bench verified. Half the time they fail. And AI-generated code produces 1.7x more issues than human-written code.
|
By Matthew LeRay
The Observability Gap shows up the moment you try to reproduce a production bug locally. Your traces tell you a request was slow. Your logs tell you which line printed. Neither tells you what was actually on the wire: the headers, the JSON body, the surprise field your client started sending last Tuesday. Until now, closing that gap meant SSHing to a node, attaching a debugger, or shipping a sidecar through change review.
|
By Ken Ahrens
We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.
|
By Speedscale Team
This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.
Sponsored Post
Part of the motivation for this post is how cloud API economics are shifting: Anthropic is moving large enterprise customers toward per-token, usage-based billing (unbundled from flat seat fees), which makes "always call the API" a moving cost line for teams at scale. A hybrid or local layer is one way to keep spend bounded while you still use premium models where they matter.
|
By Bailey Ahrens
Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.
|
By Matthew LeRay
A customer pings support: “I tried to check out twice this morning and got a 500 each time, but it works fine for everyone else.” The session ID is in the email. You have full request/response capture in your environment, you have Datadog Synthetics already running browser checks against the same flow, and you still spend the next two hours grepping logs because none of those tools let you say “show me just this user’s requests, in order, and re-run them.”
|
By Josh Thornton
The terminal is a developer’s most trusted tool. It sees your source code, your secrets, the commands you run in production. When a terminal adds AI and cloud features, it’s worth asking what it’s doing on the network.
|
By Speedscale
AI can write a feature in seconds, but where are you testing it? Sending production traffic, API payloads, and auth headers to a third-party SaaS is a massive security risk. In this video, we break down why the Bring Your Own Cloud (BYOC) model is the ultimate fix for DevSecOps. Learn how to safely test AI-generated code against real production traffic entirely within your own VPC or Kubernetes cluster. No data leaks, no massive DLP pipelines, and no endless masking rules.
|
By Speedscale
Your logs showed 500 errors. The traces showed the dependency graph. Neither showed the actual bug, a DEL control character getting appended to the query string. This is how I found it. In this video I walk through Speedscale BYOC (bring your own cloud): capture real production traffic, store it in your own Elasticsearch cluster inside your VPC, pull it down locally with a single script, and reproduce the exact bug using proxymock. The data never leaves your environment.
|
By Speedscale
Learn how to generate a client SDK for a production service when you have no documentation, no OpenAPI spec, and no remaining team knowledge of the original Ruby code. This demo shows you how to capture real production data from a running app and transform it into a functional Java client library in minutes. Visit proxymock.io OR speedscale.com to learn more.
|
By Speedscale
Stop wasting money on AI API tokens in your local, test, and CI environments. In this demo, I show how to record real LLM responses and replay them for free using proxymock and stop surprise bills.
- June 2026 (5)
- May 2026 (18)
- April 2026 (27)
- March 2026 (22)
- February 2026 (18)
- January 2026 (31)
- December 2025 (13)
- November 2025 (14)
- October 2025 (13)
- September 2025 (17)
- August 2025 (14)
- July 2025 (8)
- June 2025 (1)
- May 2025 (4)
- April 2025 (16)
- March 2025 (8)
- February 2025 (2)
- January 2025 (7)
- December 2024 (16)
- November 2024 (9)
- October 2024 (25)
- September 2024 (12)
- August 2024 (9)
- July 2024 (4)
- June 2024 (4)
- May 2024 (3)
- April 2024 (5)
- March 2024 (3)
- February 2024 (3)
- January 2024 (1)
- December 2023 (6)
- November 2023 (2)
- October 2023 (5)
- September 2023 (3)
- August 2023 (12)
- July 2023 (3)
- June 2023 (3)
- May 2023 (3)
- April 2023 (3)
- March 2023 (7)
- February 2023 (6)
- January 2023 (4)
- December 2022 (2)
- November 2022 (4)
- October 2022 (2)
- September 2022 (3)
- August 2022 (5)
- July 2022 (5)
- June 2022 (2)
- May 2022 (4)
- April 2022 (4)
- March 2022 (4)
- February 2022 (15)
- January 2022 (6)
- December 2021 (3)
- November 2021 (4)
- October 2021 (5)
- September 2021 (5)
- August 2021 (6)
- July 2021 (5)
- June 2021 (6)
- May 2021 (5)
- April 2021 (2)
- March 2021 (5)
- February 2021 (2)
- January 2021 (2)
- November 2020 (1)
Continuous Resiliency from Speedscale gives you the power of a virtual SRE-bot working inside your automated software release pipeline. Forecast the real-world conditions of every build, and know you’ll hit your SLO’s before you go to production.
Feed Speedscale traffic (or let us listen) and we’ll turn it into traffic snapshots and corresponding mock containers. Insert your own service container in between for a robust sanity check every time you commit. Understand latency, throughput, headroom, and errors -- before you release! The best part? You didn’t have to write any scripts or talk to anyone!
Automated Traffic Replay for Every Stakeholder:
- DevOps / SRE Pros: Understand if your app will break or burn up your error budget before you release.
- Engineering Leads: Let Speedscale use traffic to autogenerate tests and mocks. Introduce Chaos testing and fuzzing.
- Application Executives: Understand regression/performance, increase uptime and velocity with automation.
Before you go to production, run the projection.