# context Context is information characterizing an entity's situation in computing and information systems, used to influence interpretation or system behavior. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## When the team plays, Pix pauses: what the World Cup teaches us about outliers DevFeed: [When the team plays, Pix pauses: what the World Cup teaches us about outliers]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-17T15:17:19Z Content type: article Language: en Sources: [Nubank]() Topics: [data](), [context](), [Transactions](), [Risk](), [reliability]() Tags: [ai-research](), [context](), [data](), [data-science-machine-learning](), [patterns](), [payment](), [transactions]() ### AI overview The article uses World Cup matches as an example of how coordinated changes in everyday behavior can create outliers in payment data. It explains that instant-transfer volume drops during a match, returns after the final whistle, and may show a halftime pattern, emphasizing that anomalies must be assessed against the normal rhythm of transactions and their context. ### Source excerpt How World Cup matches turn everyday payment patterns into outliers and what those anomalies can teach us about data, risk, and reliability The post When the team plays, Pix pauses: what the World Cup teaches us about outliers appeared first on Building Nubank. ## Introducing Astra for Law DevFeed: [Introducing Astra for Law]() Original publisher: [Read original article]() Published: 2026-09-17T00:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [gpt-6-astra](), [OpenAI](), [Artificial Intelligence](), [API](), [configuration](), [context](), [Benchmark](), [ChatGPT]() Tags: [ai](), [api](), [astra](), [benchmark](), [capabilities](), [company](), [gpt-6-astra](), [openai](), [privacy]() ### AI overview OpenAI introduces Astra for Law, a legal AI foundation combining GPT-6 Astra with tailored settings, tools, context, legal search, privacy controls, and ecosystem plugins. It is designed for law firms and legal technology companies to build applications and workflows, with API access for customers such as Harvey and Legora. ### Source excerpt OpenAI for Law brings frontier intelligence for law, custom firm workflows, connected legal data sources, and legal-grade controls for confidential client work. ## 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 ## 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 ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [context](), [Tech Lead]() Tags: [adoption](), [business-analyst](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [manager](), [tech-lead](), [user-experience]() ### AI overview A first-person account of the Business Analyst role at Nubank, describing how BAs connect data analysis, context, and experimentation to product and business decisions. The article emphasizes clarifying trade-offs, investigating metrics, and collaborating with product and technical roles. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## 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. ## OpenTelemetry proposes environment variables for context propagation across processes DevFeed: [OpenTelemetry proposes environment variables for context propagation across processes]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-11T11:01:22Z Content type: article Language: en Sources: [Blog on OpenTelemetry]() Topics: [context](), [tracing](), [OpenTelemetry](), [Processes](), [CI/CD](), [data-processing]() Tags: [ci-cd](), [data-processing](), [environment-variables](), [opentelemetry](), [processes](), [spans](), [tracing](), [w3c]() ### AI overview The OpenTelemetry Specification has a release candidate for using environment variables to carry trace context and baggage between processes. The article explains how this can connect spans across workflow runners, shells, build tools, test processes, and similar workloads when protocol headers or message metadata are unavailable, and requests feedback before the specification becomes Stable. ### Source excerpt A trace does not always cross a network boundary. A workflow runner starts a shell, the shell launches a build tool, and the build tool starts test processes. Batch and data-processing systems create similar chains of child processes. Without a shared way to pass trace information across these boundaries, spans from each process can end up in separate traces. If context propagation is new to you, it is the mechanism that carries information from one service or process to the next. For tracing, this includes the trace and span identifiers that let new spans join the same trace. It can also carry baggage: application-defined key-value pairs that are passed to downstream work. ## 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. ## Build your own company brain: the enterprise AI playbook from Stripe's engineering team | Sharadh Krishnamurthy DevFeed: [Build your own company brain: the enterprise AI playbook from Stripe's engineering team | Sharadh Krishnamurthy]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-07T12:04:19Z Content type: article Language: en Sources: [Lenny's Newsletter]() Topics: [stripe](), [Artificial Intelligence](), [AI Agent](), [context](), [Developer experience](), [data](), [systems]() Tags: [agent](), [ai](), [data](), [developer](), [developer-experience](), [governance](), [platform](), [stripe](), [systems]() ### AI overview A podcast episode about Stripe's internal AI agent, Kai, and how the company scaled it across more than 10,000 employees. The discussion covers building the agent in-house, governance through projects, safe data access, infrastructure, skills, evaluation, telemetry, and lessons from incidents involving production systems. ### Source excerpt Watch now | 🎙 Learn how Stripe scaled AI to everyone at a complex global company by building context, governance, and a shared skills platform directly into their internal agent ## 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). ## Ila Kumar: Innovating with communities DevFeed: [Ila Kumar: Innovating with communities]() Original publisher: [Read original article]() Author: Gitana Savage | MIT News correspondent Published: 2026-09-01T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [digital](), [context]() Tags: [artificial-intelligence](), [assistive-technology](), [child-welfare](), [communities](), [digital](), [engineering-systems](), [graduate-postdoctoral](), [ila-kumar](), [lifelong-kindergarten](), [media-lab](), [mental-health](), [mit-graduate-students](), [profile](), [psychology](), [school-of-architecture-and-planning](), [students](), [tech-for-good](), [technology](), [technology-and-society]() ### AI overview MIT PhD student Ila Kumar researches community-based design, involving young people who have experienced childhood trauma in shaping digital technologies intended to support healing, connection, independence, and well-being. ### Source excerpt The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being. ## 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. ## 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 ## Anger, Anxiety and Agency DevFeed: [Anger, Anxiety and Agency]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-08-24T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [context]() Tags: [agents](), [ai](), [emotion](), [thoughts](), [work]() ### AI overview The author argues that anxiety and uncertainty are more constructive responses than anger to AI and agents' effects on the technology industry. They discuss concerns about changing professional skills, how productivity gains may be distributed, and tensions between companies and large AI labs. ### Source excerpt Sean Goedecke wrote a post arguing that you should never be angry at work -- a post with which I strongly agree. Anger can be a useful signal, but being angry at work rarely improves the situation. More often, it makes life worse for the people around you, many of whom have no more power over the source of your anger than you do. I did learn that lesson, but it did not come naturally. One thing in particular that I learned is that in a company there is a shared vision, and if you don't agree with it and are not in a position to change it, you should not start a mutiny, not even a small-scale one. Nothing good comes from that. In the discussion around that topic, one of the most upvoted comments on the Lobsters thread asked a question I had to think about quite a bit: How can you work in tech right now and not be angry? In the context of the thread, this was clearly also about AI and agents. For me, the emotions I would expect in tech vis-a-vis these new developments are disorientation and anxiety, but not anger. Anxiety as an emotion does not require someone to blame. Right now, I find it reasonable to feel anxious about an uncertain future. Who knows what our professions will turn into and what kind of world my kids will find themselves in when they enter the workplace? And if you've been in the industry for a long time, will the skills you've spent years acquiring still matter? But anger is different from anxiety because anger needs to be directed somewhere. The feeling of anger suggests that somebody or something is doing something to you. Who are you going to be angry at and why are you angry in the first place? One narrative that is pretty pervasive is that if AI will usher in productivity gains, those gains are going to benefit companies rather than employees. And well at least someone at Meta wants that. Yet I also find that plenty of people in leadership positions express doubt about AI. They see that an increasing share of their costs is being funneled direc ## TBM 437: Tokens, Hours, Points, and Other Curious Proxies DevFeed: [TBM 437: Tokens, Hours, Points, and Other Curious Proxies]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-08-17T23:04:04Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [Artificial Intelligence](), [context]() Tags: [ai](), [comparisons](), [measurement](), [metrics]() ### AI overview An opinion article examines the difficulty of measuring the return on AI token usage. It compares tokens with hours and flow metrics, arguing that measurement depends on a broader theory of value and can favor short-term, easily measured use cases. ### Source excerpt Everyone is talking about "return on tokens." Vendors love it (as long as the news is good). ## Knowledge Lifecycle: Capture, Organize, and Archive Team Knowledge DevFeed: [Knowledge Lifecycle: Capture, Organize, and Archive Team Knowledge]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-08-17T07:02:23Z Content type: article Language: en Sources: [Refactoring]() Topics: [context](), [save](), [Artificial Intelligence]() Tags: [archiving](), [capture](), [developers](), [knowledge-base](), [save]() ### AI overview This newsletter discusses whether AI is helping developers ship faster and introduces a three-step lifecycle for team knowledge: capture information quickly, organize it deliberately, and archive outdated material without deleting it. ### Source excerpt Monday Ideas -- Edition #221 ## Agentic Engineering 101 DevFeed: [Agentic Engineering 101]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-08-12T05:01:48Z Content type: tutorial Language: en Sources: [Craft Better Software]() Topics: [Loop Engineering](), [Prompt Engineering](), [context](), [Artificial Intelligence]() Tags: [agent](), [agentic](), [agentic-engineering](), [agents](), [ai](), [context-engineering](), [engineering](), [prompt](), [prompt-engineering]() ### AI overview This tutorial introduces agentic engineering as a set of layers around an AI model. It describes prompt engineering, context engineering, harness engineering, loop engineering, graph engineering, and memory engineering, with detailed guidance in the supplied text on prompt design and context curation. ### Source excerpt Prompt vs Context vs Harness vs Loop vs Graph Engineering ## Engineering Managers' Involvement in Product Decisions DevFeed: [Engineering Managers' Involvement in Product Decisions]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-08-11T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [Product Management](), [context](), [Claude](), [Claude Code]() Tags: [claude](), [claude-code](), [context](), [engineering](), [engineering-manager](), [product-management]() ### AI overview The article argues that engineering managers should participate more actively in product decisions and business discussions. It cites a survey of more than 950 engineering managers, finding that 45% of experienced managers were barely involved and only 16% were very involved in product decisions. ### Source excerpt Only 1 out of 6 EMs is involved in product decisions ## 5 Rules for Building AI Agents That Work in Production | Nan Yu & Jacob Shumway DevFeed: [5 Rules for Building AI Agents That Work in Production | Nan Yu & Jacob Shumway]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-08-09T13:05:31Z Content type: article Language: en Sources: [Behind the Craft]() Topics: [AI Agent](), [context](), [Tool]() Tags: [agents](), [ai](), [ai-agent](), [ai-agents](), [building](), [context](), [evals](), [production](), [tools]() ### AI overview A behind-the-scenes account of building a production AI agent end to end, including providing tools to find needed context and using evals to measure output quality. ### Source excerpt A behind-the-scenes look at building a production AI agent end to end, including how to give it tools to find the context it needs and use evals to measure output quality. 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