# context Published articles for context. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Mem0 joins the Vercel Marketplace DevFeed: [Mem0 joins the Vercel Marketplace]() Original publisher: [Read original article]() Author: Tony Pan Published: 2026-09-16T17:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Artificial Intelligence](), [App](), [context](), [API keys](), [Command-line interface](), [Environment Variables]() Tags: [ai](), [ai-agents](), [app](), [billing](), [cli](), [context](), [environment-variables](), [free](), [giving](), [install](), [integration](), [vercel](), [volume]() ### AI overview Mem0 is now a native integration on the Vercel Marketplace, providing long-term memory for AI agents and apps across sessions. The integration includes billing through Vercel, automatic provisioning of a scoped Mem0 project and API key, and environment-variable configuration. Mem0 offers a free plan with usage limits and a $20-per-month plan for higher volume. ### Source excerpt Mem0 is now available as a native integration on the Vercel Marketplace, giving your AI agents and apps long-term memory. Mem0 remembers user preferences, facts, and context across sessions, so your app stops starting from scratch. Install from the Marketplace with integrated billing on your Vercel invoice, no separate account or key management. A scoped Mem0 project and API key are provisioned automatically and added to your project as environment variables, including MEM0_API_KEY. Mem0 offers a free plan with usage limits and a $20 per month plan for higher volume. To see it end to end, deploy the eve Memory Agent template, an eve agent with long-term memory powered by Mem0. Get started with Mem0 on the Vercel Marketplace or through the Vercel CLI, available to customers on all plans. Read more ## Anthropic merges Claude Chat and Cowork into one interface DevFeed: [Anthropic merges Claude Chat and Cowork into one interface]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T16:46:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [anthropic](), [Claude]() Tags: [ai-agents](), [ai-infrastructure](), [anthropic](), [chat](), [claude](), [connectors](), [context](), [developer-tools](), [tools](), [workflow]() ### AI overview Anthropic is merging Claude Chat and Cowork into a single interface, allowing conversations to handle simple questions, multi-step projects, connected tools, and background execution. Claude Docs and Claude Slides are launching in beta on paid plans, while Claude Design is moving into conversations. ### Source excerpt Using Claude for anything beyond a quick question has always started with a routing decision to use Chat or Cowork? The post Anthropic bet users were choosing wrong. So it removed the choice. appeared first on The New Stack. ## Khan Academy's Engineering Principles DevFeed: [Khan Academy's Engineering Principles]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-06T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [context](), [Concurrent Programming]() Tags: [communication](), [company](), [context](), [eng-leads](), [engineering](), [news](), [team]() ### AI overview Ben Kamens explains how growing companies can reduce the impact of communication failures by documenting shared engineering principles. Khan Academy's principles give team members context for making decisions amid ambiguity and changing priorities. ### Source excerpt ⭐ Read about our 2019 revision of the principles! ⭐ By Ben Kamens You know those super-frustrating movie ... Read more ## How LLMs Handle Memory Through Context and Surrounding Applications DevFeed: [How LLMs Handle Memory Through Context and Surrounding Applications]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-15T15:31:12Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Large Language Model](), [context window](), [App](), [long-context]() Tags: [article](), [context](), [context-window](), [cost](), [latency](), [llms](), [memory]() ### AI overview LLMs do not usually retain personal or persistent memory between interactions. Surrounding applications create the appearance of memory by storing messages, maintaining summaries, retrieving relevant information, and supplying it to the model. As conversations grow, this processing increases cost and latency, while context-window limits require older information to be removed, summarized, or stored elsewhere. ### Source excerpt In this article, we will learn how LLMs handle memory so that they are useful to end users in performing complex tasks that require conversation and holding context. ## Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI DevFeed: [Inside the AI Stack of an $8.3B AI Company's Product Team | Together AI]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-14T23:05:28Z Content type: article Language: en Sources: [Product Growth]() Topics: [Artificial Intelligence](), [context](), [repo](), [context window](), [Developer experience](), [GitHub](), [Linear]() Tags: [ai](), [context](), [context-window](), [developer-experience](), [github](), [linear](), [product](), [repo](), [team](), [tools]() ### AI overview The article examines Together AI's AI-oriented product team and describes four parts of its working stack: a shared context repository, reusable skills for research and product documentation, an orchestrator for product leaders, and agent evaluations. It explains how shared context files and workflows support collaboration across product areas, including automated weekly status updates using Linear, GitHub, and strategy documents. ### Source excerpt I got a truly AI-pilled product team to demo the 4 key tools in their stack ## Use AI to Accelerate Delivery Without Lowering Software Quality DevFeed: [Use AI to Accelerate Delivery Without Lowering Software Quality]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-09-13T04:01:35Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [Software](), [Artificial Intelligence](), [coding](), [context]() Tags: [accelerate](), [ai](), [context](), [quality](), [software]() ### AI overview An opinion piece arguing that AI should speed delivery without reducing software-quality standards, using engineering judgment as scalable context, constraints, and checks. ### Source excerpt AI should accelerate delivery, not lower your standards. Turn engineering judgment into context, constraints, and checks that scale ## Designing Reliable AI Agent Memory for Stale Facts and Policy Changes DevFeed: [Designing Reliable AI Agent Memory for Stale Facts and Policy Changes]() Original publisher: [Read original article]() Author: Raul Junco Published: 2026-09-12T12:10:58Z Content type: tutorial Language: en Sources: [System Design Classroom]() Topics: [AI Agent](), [context window](), [context]() Tags: [ai-agent](), [context](), [context-window](), [memory]() ### AI overview This article explains that AI agent memory should be treated as evidence rather than truth, especially when policies or other facts change. It recommends attaching version, scope, source, and authoritative-data checks to memory, while keeping the context window limited to the information needed for a task. ### Source excerpt Learn how to design reliable AI agent memory that handles stale facts, policy changes, scoped retrieval, conflict resolution, and safe deletion. ## A Day of Sensemaking After Starting a New Job DevFeed: [A Day of Sensemaking After Starting a New Job]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-09-11T02:16:25Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [Notion](), [context](), [Tool]() Tags: [context](), [notion](), [product](), [workflows]() ### AI overview The author describes a day spent understanding a new product, its workflows, history, people, artifacts, and accumulated context. They use a subway-map metaphor and Notion tables to organize journeys, handoffs, data sources, and other materials, while questioning which information remains valid. ### Source excerpt I recently started a new job, which means I'm spending a lot of my time trying to figure out how everything works: the product, the workflows, the history, the people, the artifacts, and all the context that everyone else has accumulated over time. ## How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory with PHP DevFeed: [How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory with PHP]() Original publisher: [Read original article]() Author: Amanda Lange, Matthew Setter Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Composer](), [Large Language Model](), [context](), [PHP](), [Tutorial](), [Orchestration](), [OpenAI]() Tags: [agent](), [ai](), [api](), [code](), [context](), [conversation-memory](), [developer-insights](), [environment-variables](), [how-to](), [llm](), [logging](), [ngrok](), [orchestration](), [php](), [phpstorm](), [voice-api]() ### AI overview This tutorial shows how to build a PHP service with Open Swoole that uses Twilio Conversation Memory to preserve caller context, preferences, and action history across separate inbound calls. It covers the required accounts, tools, environment variables, Composer setup, OpenAI integration, logging, and Memory Store configuration. ### Source excerpt In this tutorial you'll make a PHP service using Open Swoole that retains caller context, preferences and action history across multiple separate inbound calls. ## AI Coding Tip 035 - Write Skill Descriptions in Three Sentences DevFeed: [AI Coding Tip 035 - Write Skill Descriptions in Three Sentences]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-09-06T01:34:09Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding]() Tags: [agent](), [ai](), [ai-coding](), [context](), [how-to](), [skills]() ### AI overview This tutorial recommends writing each skill description in three sentences: when to read it, the situation that triggers it, and what the skill does. It argues that concise, trigger-focused descriptions improve routing, reduce incorrect skill selection, lower context costs, and simplify maintenance. ### Source excerpt TL;DR: Split every skill description into three sentences: when to read it, when to use it, and what it does. Common Mistake ❌ You write a skill description as one long paragraph. It explains everyth ## Second Brains in the AI era: still worth it? DevFeed: [Second Brains in the AI era: still worth it?]() Original publisher: [Read original article]() Published: 2026-09-06T00:00:00Z Content type: opinion Language: en Sources: [Mutt0-ds Notes]() Topics: [Artificial Intelligence](), [context](), [AI Agent](), [Graphs](), [Obsidian]() Tags: [ai](), [ai-agent](), [context](), [knowledge-graph](), [markdown](), [obsidian]() ### AI overview The article argues that maintaining a personal knowledge base, or "second brain," is more valuable in the AI era because it gives AI assistants structured, connected context. It suggests that linked notes resemble graph memory and can help agents navigate complex information, while large unstructured collections may increase confusion and hallucinations. ### Source excerpt I've been working on my second brain(s) for years now. Brains, plural, because I had to create new ones, for example when I changed jobs...... ## TBM 437: AI and the Recontextualization Tax DevFeed: [TBM 437: AI and the Recontextualization Tax]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-09-03T00:17:23Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [Artificial Intelligence](), [context](), [meetings]() Tags: [ai](), [context](), [meetings]() ### AI overview The article argues that AI may reduce the organizational cost of recontextualizing complex information into simpler formats. It says AI can help with recontextualization and exception hunting when given sufficiently current raw context, allowing teams to work in varied ways while presenting information for different stakeholder audiences. ### Source excerpt Here's an area where I am optimistic about AI (for now, at least). ## Sega-owned Rovio is shutting down its Copenhagen studio DevFeed: [Sega-owned Rovio is shutting down its Copenhagen studio]() Original publisher: [Read original article]() Author: Diego Argüello Published: 2026-09-01T15:37:36Z Content type: news Language: en Sources: [gamedeveloper]() Topics: [Mobile]() Tags: [acquisition](), [company](), [context](), [mobile](), [news](), [project](), [time]() ### AI overview Sega-owned Rovio is closing its Copenhagen studio after canceling the mobile game Sonic Blitz. The exact number of redundancies has not yet been confirmed. ### Source excerpt The closure follows the cancellation of Sonic Blitz and the exact number of redundancies are yet to be confirmed. ## What Happens Inside an AI Chatbot Between Enter and the First Word? DevFeed: [What Happens Inside an AI Chatbot Between Enter and the First Word?]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-31T15:31:20Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [AI Chat](), [Large Language Model](), [context](), [Caching](), [Streaming](), [Tool]() Tags: [ai](), [caching](), [chat](), [context](), [llm](), [streaming](), [tools]() ### AI overview This article explains what happens inside an AI chatbot between submitting a follow-up question and receiving the first generated word. It covers prompt assembly, safety checks, token processing, conversation history, shared computing, prefill and decode, caching, streaming, guardrails, and tool execution. ### Source excerpt In this article, we are going to look at this entire journey in detail. ## Keeping credentials out of an AI agent's context with Relay DevFeed: [Keeping credentials out of an AI agent's context with Relay]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [prompt injection](), [API](), [context](), [OAuth]() Tags: [agent](), [ai-agent](), [api](), [context](), [credentials](), [prompt-injection](), [third-party]() ### AI overview The article explains how WorkOS Relay keeps third-party API credentials out of an AI agent's context. Relay proxies outbound calls and injects credentials at the boundary, reducing the opportunity for prompt injection to steal or exfiltrate bearer tokens. The document says Relay shipped on August 6, 2026 and is in early access. ### Source excerpt Relay proxies an agent's third-party API calls and injects the credential at the boundary, so prompt injection has no token to steal and nowhere to send it. ## AI Coding Tip 034 - Stop Hoarding Rules in Your AGENTS.md DevFeed: [AI Coding Tip 034 - Stop Hoarding Rules in Your AGENTS.md]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-28T18:10:58Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding](), [Code]() Tags: [agents](), [ai-coding](), [coding](), [context](), [developer](), [how-to](), [mcp](), [skills](), [test]() ### AI overview The article advises developers to regularly audit AGENTS.md files and skills, remove rules that no longer matter, and test whether instructions still improve agent performance. It highlights context bloat, stale skills, dead MCP servers, plugins, and hooks as ongoing maintenance costs. ### Source excerpt TL;DR: Audit your AGENTS.md and skills on a schedule, or you keep paying context rent on rules the model has outgrown. Common Mistake ❌ Every time the AI does something wrong, you add a rule to stop ## Putting an agent in a shared thread makes it a colleague DevFeed: [Putting an agent in a shared thread makes it a colleague]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T15:16:58Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Slack]() Tags: [agent](), [agents](), [ai](), [context](), [slack](), [threads]() ### AI overview The article argues that placing AI agents in shared Slack threads makes them function like colleagues, creating social and operational questions around addressing, assignment, permissions, presence, and audience. Drawing on the experience of running more than 80 team agents, it presents mention-only responses and a distinction between room membership and assignment as practical rules. ### Source excerpt Internal AI moved from dashboards into Slack threads. That changes addressing, mandate, presence, and audience -- questions that used to apply only to people. ## The Context Tax of Agentic Development DevFeed: [The Context Tax of Agentic Development]() Original publisher: [Read original article]() Author: A Talhan Published: 2026-08-25T11:01:02Z Content type: opinion Language: en Sources: [Expedia]() Topics: [Agentic development](), [Development](), [networking](), [data]() Tags: [agentic](), [agentic-ai](), [agentic-development](), [agents](), [context](), [context-engineering](), [development](), [engineering-management](), [networking](), [software-engineering](), [teamwork]() ### AI overview An Expedia Group engineering team describes how hidden context can cause agentic development to accelerate technically plausible work aimed at the wrong system. Using AI Workbench's multi-environment model handling as an example, the article argues that context must be explicit and structured so humans and agents share the same understanding. ### Source excerpt Expedia Group Technology -- EngineeringMissing context used to slow a team down, with agents in the loop it speeds up the wrong work insteadPhoto by Harley-Davidson on Unsplash When we started using agents more seriously inside the team, the first bottleneck was not code generation -- It was context coordination. That sounds like a documentation problem, but it did not feel like one. It felt like a delivery problem. A human engineer who is missing context usually slows down. They ask someone, search through old notes, or wait for the next sync. An agent does not always slow down. It can keep moving and produce work that is technically plausible, well formatted, and aimed at the wrong reality. That is the agentic velocity trap: unclear context does not just delay work; it can accelerate the wrong work. Rick Fast recently wrote about the broader Expedia Group™ platform shift toward agent-friendly interfaces and operating surfaces. This is the ground-level version from one team: what did we have to change in our own planning loop so humans and agents could work from the same understanding? The problem was hidden context One of the clearest examples came from multi-environment handling in AI Workbench (our web console for machine learning (ML) artifacts and workload management). From the outside, an AI Workbench URL appeared to represent one backend environment. Under the hood, it was backed by another. A team saw their model in the UI, reasonably assumed it existed in the environment implied by the URL, and then hit "model not found" when downstream jobs queried that expected backend. Internally, the concrete case was a machine learning scientist team onboarding a model. It appeared in the .prodA AI Workbench URL, but that deployment was serving model registry prodB data rather than model registry prodA data. The platform services were not simply broken. The hidden semantic mapping was because of networking and access nuances. That distinction matters. A human team lost ## Context Engineering for Coding Agents DevFeed: [Context Engineering for Coding Agents]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-08-25T05:01:37Z Content type: tutorial Language: en Sources: [Decoding ML]() Topics: [context window](), [AI-assisted coding](), [coding](), [Claude](), [codex]() Tags: [agents](), [claude](), [codex](), [coding-agents](), [context](), [context-engineering](), [context-window](), [harness]() ### AI overview This tutorial explains context engineering for coding agents, focusing on memory, skills, LSP servers, compaction, and feedback loops. It presents these techniques as ways to keep an agent's context high-signal and improve coding-agent performance. ### Source excerpt The 4 harness components that keep your context window high-signal. ## How Srini Raghavan helped $3.4B SaaS Giant Freshworks Embrace the AI PDLC DevFeed: [How Srini Raghavan helped $3.4B SaaS Giant Freshworks Embrace the AI PDLC]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-08-24T23:16:09Z Content type: article Language: en Sources: [Product Growth]() Topics: [Software as a service](), [AI Agent](), [cursor](), [Design system](), [context](), [repo]() Tags: [ai](), [ai-agent](), [context](), [cursor](), [design-system](), [repo](), [saas]() ### AI overview The article summarizes how Freshworks moved from a six-month release process to a two-week cycle under Chief Product Officer Srini Raghavan. It describes an AI product development lifecycle built on structured data, a parsable design system, documented coding standards, a single source repository, contextual knowledge hubs, reusable AI builder artifacts, governed AI agents, and an evaluation phase. ### Source excerpt + Why he uses Grok models, and the future of the "Product Builder" role ## Measuring how coding-agent context delivery affects results per token DevFeed: [Measuring how coding-agent context delivery affects results per token]() Original publisher: [Read original article]() Author: Raymond Gitonga Published: 2026-08-21T13:36:56Z Content type: opinion Language: en Sources: [OLX]() Topics: [Artificial Intelligence](), [coding](), [Back end](), [Testing]() Tags: [agent](), [ai](), [ai-agent](), [ai-experiments](), [backend](), [coding](), [comparative-studies](), [context](), [cost](), [software-engineering](), [testing]() ### AI overview The article describes an experiment comparing four ways of delivering context to a coding agent. The same task, model, scoring method, and reference files were used across three runs of each setup, with the goal of measuring results relative to token cost. The task was adding a notification worker to a mature backend system spanning three codebases and approximately 18-20 files. ### Source excerpt I gave a coding agent the same job four different ways and measured what each cost. The packaging barely mattered, but something else did. At OLX, we've been pushing to get AI tools into everyday engineering work. This has enabled us to have faster delivery and better test coverage, with less time spent on the mechanical parts. Our biggest constraint, however, is tokens. Tokens being a finite resource, we keep running up against our allocated quota. So the real question is how to get the best results per token spent. There's no shortage of advice on how to feed context into agents: put everything into one prompt, split it into folders, or give one orchestrator the whole picture and let it delegate. But opinions vary on which way is better and which produces the best results relative to cost. So I decided to measure it. One job, four setups, three runs each: same model, same task, same scoring. The only thing that changed was how the agents got their context. The Setup The job was to add a notification worker to one of our fairly mature backend services. In plain terms, the worker watches for things happening in the system, decides who should get a notification, and logs the outcome. The worker lives in one service, but the change touched around 18-20 files across three codebases that talk to each other, tests included. All three have years of history and firm architectural conventions. In short: a realistic change in a mature system, not a toy task. Every setup got the same reference files, written once, and mostly derived from each codebase's own AGENTS.md file. They cover a repo map, one deep dive per codebase, the coding and testing conventions, and the contracts between the services. The CLAUDE.md files you'll see in the setups below are different: those are each setup's entry point, and part of what varied. The reference files were identical in every setup. Only the delivery changed. 1. Default: One manager owns everything. It keeps context in memory and spins ## AI Prototyping in 2026: The PM Field Guide DevFeed: [AI Prototyping in 2026: The PM Field Guide]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-08-19T12:19:16Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Artificial Intelligence](), [prompt](), [Tool](), [Security](), [pixel]() Tags: [2026](), [ai](), [context](), [field](), [guide](), [pixel](), [prompt](), [security](), [tool]() ### AI overview A field guide to AI prototyping that compares tools for different jobs, presents a context prompt, recommends a security check before sharing a link, and addresses pixel-perfect handoff. ### Source excerpt I built the same CRM four times in just over an hour, live. The field guide: which tool for which job, the context prompt that beats a spec, the security check before you share a link, and the pixel-perfect handoff ## From Weeks to Hours: Inside Wix's Autonomous Bug-Fixing System DevFeed: [From Weeks to Hours: Inside Wix's Autonomous Bug-Fixing System]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-08-19T10:17:19Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [bug](), [Code review]() Tags: [architecture](), [autonomous](), [bug](), [code-review](), [context](), [database](), [jira](), [logs](), [pipeline](), [production](), [repo](), [support]() ### AI overview Wix describes the architecture behind Wix Orchestrator, an autonomous bug-fixing system designed to reduce the time from user reports to production fixes. The system automates investigation, context gathering, implementation, and code review, while requiring a human engineer to approve every fix before deployment. ### Source excerpt A user opens a support ticket. Their subscription cancellation stopped working. Somewhere inside a system with thousands of services and millions of users, something broke. Before we built Wix Orchestrator, here's what happened next: the ticket joined a queue. Days later, a support engineer picked it up, tried to reproduce the issue, contacted the customer to gather more context, and opened a Jira ticket routed to R&D. We triaged it, prioritized it against everything else on our plate,... ## What it's like to join WorkOS as a product designer DevFeed: [What it's like to join WorkOS as a product designer]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-17T19:47:49Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Artificial Intelligence](), [Model Context Protocol](), [Tooling](), [Figma](), [Slack](), [Notion]() Tags: [ai](), [connectors](), [context](), [data](), [design](), [designer](), [figma](), [mcp](), [slack](), [tooling]() ### AI overview A product designer describes joining WorkOS and using shared company context, MCP-connected data, Slack, Notion, and AI-assisted design tooling to answer product questions and onboard more quickly. ### Source excerpt What it's actually like to join WorkOS as a product designer: transparent context by default, and AI as everyday design tooling. [Next page]()