The hottest Developer Tools Substack posts right now

And their main takeaways
Category
Top Technology Topics

How I Interview Software Engineers, Post AI Code Gen.

decodebytes • 87 implied HN points • 15 Sep 25
đź•ą Technology Developer Tools
  1. Interviews should focus on real-world skills instead of memorization. Candidates need to show they can break down complex problems and work collaboratively, which is more important than just recalling syntax.
  2. It’s essential to create a friendly atmosphere during interviews. This allows candidates to feel comfortable asking for help or admitting when they don't know something, which reflects how they'll behave in a team.
  3. Diverse interview panels can reveal how candidates respond to different perspectives. This helps assess their teamwork and social skills, ensuring they contribute positively to team dynamics.

AI Unwrapped #2 | Feb 10 – Feb 24, 2026

The Product Channel By Sid Saladi • 3 implied HN points • 27 Feb 26
đź•ą Technology Developer Tools
  1. Google’s Gemini 3.1 Pro reclaimed the lead with a major reasoning jump and top benchmark scores while keeping the same API pricing, making it far stronger for logic, coding, and multimodal tasks.
  2. AI capabilities are expanding fast — models now solve PhD-level science problems, generate music from images, find long-hidden security bugs, and power new agent platforms and browser/assistant integrations.
  3. If you build products, test these new models on your hardest multi-step problems and add AI-powered checks like security reviews, because the recent reasoning gains can materially change outcomes.

Scaffolding is coping not scaling, and other lessons from Codex | OpenAI’s Thibault Sottiaux

Dev Interrupted • 9 implied HN points • 27 Jan 26
đź•ą Technology Developer Tools
  1. Widespread AI adoption comes from engineering for resilience: teams build repo-ready context, rule files, and guardrails so models become reliable teammates across iOS, Android, and backend systems.
  2. The era of humans typing syntax is fading — engineers are shifting from writing code to orchestrating and managing multiple AI agents and the handoffs between them.
  3. Don’t be loyal to one model; treat models as tools in a belt and pick the best model for each task to maximize velocity and capability.

AI tooling benchmarks: PR throughput and usage by tool (Q1 2026)

Engineering Enablement • 11 implied HN points • 21 Jan 26
đź•ą Technology Developer Tools
  1. AI-native, agentic coding tools are driving the biggest increases in PR throughput. Cursor, Claude, and GitHub Copilot showed notable quarter-over-quarter gains while Tabnine registers lower throughput, often in large enterprises.
  2. Adoption patterns vary by cadence: Copilot is the stickiest daily driver, Cursor is becoming a primary weekly workspace, and tools like Windsurf and Tabnine are used more monthly for specialized tasks.
  3. Organizations should correlate tool usage with PR throughput and measure ROI rather than counting seats alone. A multi-vendor approach and stronger practices are recommended because technical limits and policy gaps still constrain productivity gains.

The Sequence AI of the Week #773: The Week Google Turned Gemini Into an Agent Runtime

TheSequence • 28 implied HN points • 17 Dec 25
đź•ą Technology Developer Tools
  1. Google moved from just releasing models to shipping an agent runtime that coordinates and runs agents, making Gemini a platform for agent workflows.
  2. The Interactions API (Beta) and the Gemini Deep Research Agent (Preview) were released together, signaling a deliberate architectural pivot and providing both the runtime and a managed agent that uses it.
  3. Real agent systems are stateful, tool-heavy, and long-running, so most engineering effort goes into planners, tool routing, memory, retries, auditing, and UIs — the LLM call itself is the smallest piece.
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Nebius Part 1: the Smooth (Developer) Operator

Interconnected • 277 implied HN points • 17 Feb 25
đź•ą Technology Developer Tools
  1. Nebius is focused on creating a smooth experience for developers. They make it easy for developers to start using their platform without unnecessary steps, which is important for building cool AI projects.
  2. The company has a strong background thanks to its roots in Yandex, which gives it experience in running cloud services effectively. This experience helps Nebius offer a wide range of cloud solutions, not just GPU rentals.
  3. While some may worry about Nebius's Russian connections, the company has distanced itself from that past. With significant funding and a solid road ahead, it seems ready to grow and succeed free from those burdens.

OpenAI WebSocket Mode, 0x Cross-Chain API, TradeStation + Capitalize

The API Changelog • 1 implied HN point • 03 Mar 26
đź•ą Technology Developer Tools
  1. APIs are shifting from stateless REST to low‑latency, persistent connections so AI agents can orchestrate complex actions in real time.
  2. New one‑to‑many and aggregator APIs hide provider complexity behind a single, normalized endpoint, cutting integration work and speeding product development.
  3. APIs are becoming programmable operational metrics that let teams embed visibility and decision signals directly into workflows so data drives immediate action.

Technically Monthly (January 2026)

Technically • 12 implied HN points • 06 Jan 26
đź•ą Technology Developer Tools
  1. Try multiple vibe-coding tools by building the same thing so you learn their quirks, limits, and pricing before committing.
  2. Monitor AI with simple evals: study failures, use straightforward assertions instead of AI-judging-AI, and follow a loop of vibe check, spreadsheet, fixes, then targeted tests to cut hallucinations.
  3. Use AI thoughtfully at work by customizing prompts and iterating on workflows; learn prompt engineering or you risk being outcompeted by careless automation.

Five Lessons From Building AI Developer Tools

Kathy PM • 15 implied HN points • 22 Dec 25
đź•ą Technology Developer Tools
  1. AI shifts complexity rather than removing it. The mess moves from configs and docs into prompts, retries, and opaque layers, so teams must decide where to contain it.
  2. Developers want AI that manages itself quietly in the background. They don’t want to babysit agents, re-run tasks, or constantly context-switch between new dashboards and chats.
  3. Trust and integration matter more than flashy features. Predictability, consistency, and small reliable automations inside editors and pipelines make work lighter and let developers feel in control.

Pratik's Year in Review: 2025

Pratik’s Pakodas 🍿 • 8 implied HN points • 02 Jan 26
đź•ą Technology Developer Tools
  1. AI agents and skill-based subagents let you run many tasks in parallel and move work forward continuously. This shifts the role from single-threaded coder to an orchestrator who delegates, evaluates, and scales output.
  2. Building a community depends on habits and critical mass more than platform features; migrating people to a new platform requires heavy outreach, patience, and active admins. Tools help, but sustained engagement is earned through social habits, not just better functionality.
  3. Measure and design life around 'great days' by engineering conditions that produce them — prioritize sleep, consistent healthy habits, slack time, and small experiments. Small rituals and intentional choices matter more for long-term happiness than chasing big achievements.

AI and productivity: Year-in-review with Microsoft, Google, and GitHub researchers

Engineering Enablement • 13 implied HN points • 17 Dec 25
đź•ą Technology Developer Tools
  1. Lines of code is a poor measure of AI’s value — more output doesn’t equal more impact. Use broader measures like satisfaction, performance, collaboration, and efficiency to judge whether AI actually helps.
  2. AI is changing the developer role from code producer to director and validator of AI-assisted work, so hiring, career paths, and training must prioritize AI fluency, systems thinking, and judgment. Juniors might learn end-to-end problem solving faster, but only if teams preserve mentorship and opportunities to collaborate.
  3. The real wins come from enablement and focusing AI on real bottlenecks or tedious work, not from constantly switching tools or models. Also, don’t trust simple headlines — dig into context, and design tools to boost creativity and meaningful automation rather than just raw speed.

The hidden costs of pre-computing data | Chalk's Elliot Marx

Dev Interrupted • 14 implied HN points • 09 Dec 25
đź•ą Technology Developer Tools
  1. Pre-computing and storing large volumes of derived data wastes money and adds latency because most of it is never used. Shifting to real-time, incremental pipelines means you only compute what users actually need.
  2. Owning the full stack (hardware, training, and cloud) creates a competitive moat and can change leaderboard dynamics quickly. Design your systems to be model-agnostic and flexible so you don’t get locked into one provider.
  3. Typical engineering metrics like velocity or lines of code are often misleading; measure what exposes real friction, bottlenecks, and business outcomes. Use metrics to make the system legible and actionable, not just to produce executive reports.

Areas of interest (June 2023)

Peter's Newsletter • 137 implied HN points • 20 Jun 23
đź•ą Technology Developer Tools
  1. Design-to-code automation is being explored, possibly streamlining the process of translating designs into working code.
  2. Developer playgrounds, like Jupyter notebooks, are becoming more important for creative software development and experimentation.
  3. Treating agents as users opens up new possibilities in app interactions, such as assigning work or leveraging business knowledge in various contexts.

Stop managing AI. The case for proactive agents.

Kathy PM • 18 implied HN points • 24 Nov 25
đź•ą Technology Developer Tools
  1. Developers want tools that handle tasks proactively, instead of just reacting to commands. They don’t want to waste time managing tools; they want to focus on building.
  2. For AI tools to be useful, they need to understand the context of a project and work seamlessly with developers. This means recognizing patterns and anticipating needs before being asked.
  3. The future of coding tools should feel collaborative. We aim for AI that can act like a helpful teammate, reducing mental load and helping developers concentrate on creative problem-solving.

AI made us faster. That’s not the same as better.

Brick by Brick • 9 implied HN points • 24 Dec 25
đź•ą Technology Developer Tools
  1. AI coding tools have evolved into a diverse, faster set of assistants with different interaction styles, and engineers now choose which tool to use for each task.
  2. These tools speed up work but rarely produce code that’s clearly better — most AI-generated code still needs human review, polishing, or refactoring before it’s ship-ready.
  3. Engineers use AI selectively and responsibly: they get productivity and satisfaction gains while maintaining ownership of code quality and long-term maintenance.

Introducing ERC-8109 Diamonds, a Standard for Simpler Diamond Contracts

EIP-2535 Diamonds • 7 implied HN points • 31 Dec 25
đź”® Crypto Developer Tools
  1. Diamond contracts reduce on-chain complexity by exposing lots of functionality through a single address and breaking large systems into small, purpose-built facets that are easier to test, audit, and evolve.
  2. ERC-8109 simplifies and standardizes diamonds by clarifying terminology, requiring just two introspection functions, replacing the monolithic DiamondCut event with per-function events, and adding an optional, consistent upgrade function plus a clear upgrade path for existing diamonds.
  3. Compose is a practical library and tooling ecosystem that implements ERC-8109 ideas, providing reusable on-chain facets and deployment/testing tools to make building modular diamond systems straightforward for developers.

Recursive Language Models ("RLMs")

Why Now • 7 implied HN points • 09 Jan 26
đź•ą Technology Developer Tools
  1. Models suffer from "context rot" on very long inputs: attention gets diluted, positional signals degrade, and small mistakes compound over long sequences.
  2. Recursive Language Models (RLMs) handle long context by having a root model peek, create targeted context slices, spawn sub-models to summarize or process each chunk, and then combine results, so each model sees much less context.
  3. RLMs have shown strong empirical gains and cost savings on long-context benchmarks, and they could enable scalable codebase reasoning, long-running assistants, and other tasks that need effectively unlimited context.

Are developers happy yet? Unpacking the 2025 Developer Survey | Stack Overflow’s Erin Yepis

Dev Interrupted • 14 implied HN points • 02 Dec 25
đź•ą Technology Developer Tools
  1. Developer job satisfaction is improving after a recent dip, driven mainly by better autonomy and compensation, though senior engineers report higher happiness than juniors.
  2. AI tools speed up code generation but often just move the bottleneck to testing, validation, and maintenance, so teams need experienced oversight and metrics to avoid creating technical debt quickly.
  3. Large language models can be compressed and de‑censored, showing they’re easy to reverse‑engineer and repurpose, which raises new risks for model security and trust.

3 Techniques to help you optimize your code bases[Technique Tuesdays]

Technology Made Simple • 119 implied HN points • 26 Apr 23
đź•ą Technology Developer Tools
  1. Compile time evaluation can help execute functions at compile time instead of run time, saving memory and CPU time.
  2. Dead code elimination removes unused code, enhancing code readability and reducing executable size.
  3. Strength reduction is a compiler optimization technique that replaces expensive operations with simpler ones, making localized code changes easier.

The one where we vibe code holiday cards | Season 5 Finale

Dev Interrupted • 9 implied HN points • 23 Dec 25
đź•ą Technology Developer Tools
  1. MCP agents need strong safeguards: treat actions on a spectrum of reversibility and consequence, and require a human in the loop for irreversible or high‑risk operations.
  2. Engineers are still responsible for delivering proven code, not just generating it — every line of AI‑produced code must be verified and tested before shipping.
  3. Rigid engineering dogmas like mandatory review for every PR and slavish sprint rituals slow teams down. Teams should let senior engineers self‑merge low‑risk changes and audit whether safeguards prevent bugs or just block work.

Cursor vs Anthropic multi-agent coding experiments

Peter’s Substack • 2 implied HN points • 06 Feb 26
đź•ą Technology Developer Tools
  1. Use a hierarchical decomposition where high-level planners break goals into subplanners and isolated workers so complex coding tasks are split, owned, and driven to completion recursively.
  2. Coordination and correctness are the main bottlenecks for parallel agents: naive locking and expecting perfect commits cause conflicts and serialization, so robust coordination and tolerance for imperfect commits are needed to scale.
  3. Human input still matters a lot—clear, prioritized instructions, tests, and failure analysis are essential to guide agents, enforce performance and resource limits, and catch subtle bugs agents miss.

Acceleration With Consequences

Kathy PM • 7 implied HN points • 03 Jan 26
đź•ą Technology Developer Tools
  1. AI is shifting from one-off features to ongoing relationships, so tools will be judged by how they behave and fit into users' lives over weeks, not just by single outputs.
  2. Agency and control matter more than raw intelligence; the hardest design choices are about when an AI should act, when it should stay quiet, and who gets to decide.
  3. Working code alone won’t win — teams need understandable, maintainable systems and clear mental models, because loss of trust and confusing handoffs will drive people away faster than bugs.

GPT‑5.2, GLM-4.6V, Runway's GWM Worlds, GWM Avatars and GWM Robotics, Nomos 1, Devstral 2, Wan-Move, SimGym, Disco, Stripe's Agentic Commerce Suite and more

AI Brews • 10 implied HN points • 12 Dec 25
đź•ą Technology Developer Tools
  1. Large AI models are making big leaps: new releases like GPT‑5.2 and specialized models improve reasoning, code, vision, long‑context handling, and tool use, while smaller specialist models like Nomos 1 can outperform humans on hard math tasks.
  2. Agentic and commerce-focused tools are moving into the mainstream, with products and standards that let AI agents act inside apps, make purchases, and integrate into workflows (agentic commerce, foundation efforts, and Slack/agent integrations).
  3. Multimodal content and developer tooling are exploding: new video and avatar systems, motion‑controllable video models, Adobe ChatGPT integrations, visual editors, and many open‑source projects make it much easier to build and deploy creative AI applications.

Interfaces all the way down

Jinay's Substack • 94 HN points • 23 Jul 23
đź•ą Technology Developer Tools
  1. Designing high-quality interfaces is crucial for software engineers as they advance in their careers.
  2. Maintaining independence between software components through well-constructed interfaces is important to avoid technical debt.
  3. Crafting interfaces with the end user, other developers, in mind can lead to more intuitive software design.

🎉 Happy New Year! Your Top Articles of 2025 + A Big Thank You

The Product Channel By Sid Saladi • 6 implied HN points • 01 Jan 26
đź•ą Technology Developer Tools
  1. The community grew to over 10,000 subscribers and added paid subscriptions, showing strong reader support.
  2. A large library of practical AI and product management resources was published, including a 10-part Ultimate AI Guide and 101 guides for Perplexity, Claude, and ChatGPT to help PMs use AI effectively.
  3. New products and hands-on experiments were launched—GetPrompts and ProductGPT led the way, with Vibe Coding deep dives and AI browser workflow testing making real-world AI tools easier to adopt.

RDEL #28: What type of interruptions impact developer productivity most?

Research-Driven Engineering Leadership • 39 implied HN points • 05 Feb 24
đź•ą Technology Developer Tools
  1. Self-interruptions (voluntary task-switching) are more disruptive for developers than external interruptions.
  2. Contextual factors like interruption type (self vs external) and time of day have a stronger impact on disruption than task-specific factors like priority.
  3. Developers are more vulnerable to task-switching and interruptions when switching between programming and testing tasks compared to other development tasks.

Human API, Sapiom $15.75M, EU Challenges Meta

The API Changelog • 1 implied HN point • 16 Feb 26
đź•ą Technology Developer Tools
  1. AI agents are starting to make real purchases on their own as companies build secure spending APIs that turn budgets into programmable keys, letting agents buy compute, services, or physical work within human-set limits.
  2. APIs are moving beyond cloud data into the physical world and human labor — from programmable cellular networks and surgical-robot vision APIs to marketplaces that let agents hire people for last‑mile tasks.
  3. Platform control and regulation are becoming central: major platforms are tightening or restricting developer access while regulators push to keep key APIs open, reshaping how apps and agents get distribution and resources.

Google Developer Knowledge MCP, Linq $20M, FireCompass Explorer

The API Changelog • 1 implied HN point • 10 Feb 26
đź•ą Technology Developer Tools
  1. APIs are becoming the primary interface for AI and autonomous agents, shifting design and product decisions away from human‑first experiences. This lets assistants live inside existing apps and enables real‑time capabilities like voice translation.
  2. As APIs power more automation, security risks and supply‑chain exposure grow—hidden endpoints and misconfigurations can leak credentials across systems. Teams need proactive, agentic testing and stronger access controls to find and fix shadow APIs before attackers do.
  3. Enterprises are packaging complex domains behind unified APIs and tools to make AI integration practical across industries. Measuring AI‑readiness and centralizing documentation and access is becoming essential for reliable, maintainable integrations.

An Interview With Or Weis

ciamweekly • 62 implied HN points • 03 Feb 25
đź•ą Technology Developer Tools
  1. CIAM helps businesses balance security and user experience. If security is too tight, users get frustrated, while loose security can lead to risks.
  2. Without CIAM, companies waste time creating custom access control systems. CIAM makes it easier for developers to manage permissions, so they can focus on product development.
  3. The future of CIAM involves managing machine identities as much as human ones. As automation grows, businesses will need new methods to handle permissions for both types of users.

20251214

Curious futures (KGhosh) • 4 implied HN points • 14 Dec 25
đź•ą Technology Developer Tools
  1. AI is automating mundane work and reshaping jobs, but overreliance can erode core skills, personal agency, and real human connection.
  2. Geopolitical and security risks are rising as technology spreads — drones, attacks on infrastructure, and national preparedness programs show new vulnerabilities and tensions.
  3. Rapid biotech and tech advances (from universal organs to thought-prediction and nature-inspired solutions) bring big promise but also ethical and practical risks, so new innovations should be adopted cautiously.

AI won’t thrive in legacy interfaces

Dev Interrupted • 23 implied HN points • 26 Jun 25
đź•ą Technology Developer Tools
  1. AI needs better interfaces to work effectively. The old ways just can't keep up with how we now want to collaborate with AI.
  2. The command line is still really important for developers. It’s precise and helps focus on the entire system, but it needs to evolve to work well with AI.
  3. We need a whole new environment for developers that communicates clearly with AI. It should understand everyday language and give developers clear visibility into what AI is doing.

FunctionGemma, GPT‑5.2-Codex, Chatterbox Turbo, A2UI, Seedance 1.5 pro, GPT Image 1.5, SAM Audio, Wan2.6, LongCat-Video-Avatar, Mistral OCR 3, Ray3 Modify, FLUX.2 [max] and more

AI Brews • 2 implied HN points • 19 Dec 25
đź•ą Technology Developer Tools
  1. AI development is accelerating around multimodal and audio‑video capabilities, with many new models that generate or edit high‑quality video, isolate sounds, and produce expressive, lip‑synced audio.
  2. The agent and developer ecosystem is maturing fast — plugin marketplaces, open agent standards, memory‑first agents, and UI/ workflow tools are making it much easier to build, extend, and deploy agentic applications.
  3. Open‑source and specialized releases are raising the bar for core capabilities like OCR, 3D view synthesis, image generation, code/documentation automation, and semantic search, bringing more practical AI tools to developers and creators.

Frontend Hype #5 - ChatGPT FE & BE, RSC Drama, From Signals to Hooks, Hokai Edge Framework, A Life Engineered

Full Context Development • 19 implied HN points • 11 Mar 23
đź•ą Technology Developer Tools
  1. ChatGPT is not yet advanced enough to replace frontend developers, but can be useful for pre-generating basic code or refactoring.
  2. React Server Components have potential benefits for web performance and developer ease, but are not a silver bullet solution.
  3. Understanding the differences between React class components and function components with hooks can enhance React development skills.

AllSpark MVP Update

AllSpark • 19 implied HN points • 15 Apr 23
đź•ą Technology Developer Tools
  1. AllSpark MVP has new subscription flow and app store pages.
  2. Launched LFW.dev community for developers working on user-controlled data.
  3. Upcoming features include email organization tools and content packs.

Workflows Give Purpose to APIs

The API Changelog • 1 implied HN point • 31 Dec 25
đź•ą Technology Developer Tools
  1. Workflows turn abstract CRUD APIs into meaningful, user-focused operations by combining multiple low-level requests into a single higher-level action.
  2. A workflow operation like “onboard” can hide database details, perform lookups and validations, and make integration much easier for consumers.
  3. Workflows let teams adapt generic APIs to real use cases and prototype new operations quickly, and they enable non-technical people to define or iterate on behaviors without changing the underlying API.

It was never about LLM performance

Technically • 41 implied HN points • 06 Mar 24
đź•ą Technology Developer Tools
  1. It's not just about the performance numbers of large language models (LLMs). The real value lies in the experiences built on top of these models for customers.
  2. The ChatGPT interface demonstrates the importance of the overall experience over just the underlying model technology in LLMs.
  3. When considering open source LLMs, it's crucial to focus on the holistic experience that model providers offer, not just the performance metrics in comparison to closed source models.

Applying the Core 4 (Part 1)

Engineering Enablement • 12 implied HN points • 19 Jan 25
đź•ą Technology Developer Tools
  1. Use a survey to gather Core 4 metrics easily. It's designed for simplicity, so anyone can set it up.
  2. Calculate your metrics by averaging survey responses for Speed, Quality, and Impact. For Effectiveness, look at the positive responses overall.
  3. Once you have your results, compare them with industry benchmarks to see how you're doing. This helps you understand your team's performance better.