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Based on 4 episodes we listened to · September 2026
Programmers makes system design legible by tracing technical choices through failure modes, operating costs, and real product constraints.
Programmers turns engineering problems into a two-voice explanatory conversation, moving from a concrete failure or product constraint toward the architecture underneath it.
One user action, 27 database queries: your next system design answer could use that warning.
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Short reviews from the PlayNext desk, based on the episodes we processed.
A 15-minute chip order becomes a grounded lesson in spatial search, consistency, scale, and product judgment.
A late-night craving for jalapeno cheddar kettle-cooked chips turns into a focused look at what makes 15-minute delivery hard to design well. The discussion tracks the practical machinery behind the promise, from micro-warehouse inventory and spatial indexes to Haversine…
A grounded guide to trimming Lambda costs with memory tuning, Graviton, and less custom code.
AWS Lambda Pricing frames serverless costs as something engineers can actually tune, not just a bill to absorb. It starts with the familiar tension between paying only for use and still losing money to wasted milliseconds, then walks through practical levers: reading logs,…
A clear, practical look at how Scrapingdog handles the tedious mechanics of web data collection.
Web scraping sounds less intimidating here than it often does. This single episode frames the Scrapingdog API as a practical answer to the familiar chore of copying information from websites by hand, then keeps the explanation grounded with a simple digital-assistant comparison.
A clear tour of how Iceberg connects changing schemas, large-scale storage, and unified data workloads.
Apache Iceberg receives a clear, practical treatment as a smart layer for data lakes, with the episode framing it around the push to bring batch and streaming work closer together.
A sharp reminder that small assumption mismatches can turn one request into a system-wide failure.
The episode lands on a clear point: in modern cloud systems, code can be sound and still fail badly when timing assumptions slip. It keeps the discussion grounded in deadlines, retries, and retry storms, and the three-layer retry example makes the failure mode feel immediate…
Specialists walking through software's tradeoffs, one system at a time.
Founders and engineers on AI coding agents like Claude Code, and supply chain security.
The engineering decisions behind code, agents, and control.
Python tools, practical trade-offs, and the occasional programming joke.
Matt Stratton interviews DevOps practitioners on security, platform engineering, and trusting AI-written code.
This episode explores how perfect code can fail in distributed systems due to hidden assumptions and network unreliability.
The episode opens with a Tuesday-night GoPuff-style chip craving and a 15-minute promise that depends on a micro-warehouse finding the exact purple-bag jalapeno cheddar kettle chips.
AWS Lambda Pricing argues that the real bill driver is GB-seconds, not the flat request fee, which is described as about 20 cents per million invocations.
An explanation of the differences between HTTP/1, HTTP/2, and HTTP/3 using a road and lanes analogy.
Explains why AWS SSO login doesn't update the .aws/credentials file and how temporary credentials work.
This episode explores web scraping using the Scraping Dog API, covering how it handles challenges like rotating proxies and headless browsers.
Apache Iceberg is framed as an open table format for data lakehouses that tries to unify batch and streaming data from the same source.
Exploring Medusa, an open-source headless commerce platform, and its benefits for developers and startups.
Programm ers examines how FastMail has managed its own hardware for more than two decades, using different storage tiers for email metadata and message content.
Programmers explains how open-source AI combines LLMs such as Llama 3.3, Mistral, Qwen 2.5, Phi-3, and Gemma 2 with embedding models and PostgreSQL extensions.
This episode explains how GPT uses generative pre-training and transformer architecture to improve language understanding.
A deep dive into navigating Hugging Face's model hub, selecting and using pre-trained machine learning models for software projects.
A deep dive into ChatGPT's new Canvas feature for Python developers, comparing it with CodeInterpreter.
Professor Damodaran’s July 17, 2023 assessment contrasts a resilient first half with the earlier consensus of inflation, recession, and market collapse.