Zero context switching for developer workflow

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗸𝗶𝗹𝗹𝗲𝗿 𝗳𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀? Context switching. You send a colleague a "quick" Slack message. Takes you 𝟱 𝘀𝗲𝗰𝗼𝗻𝗱𝘀, but it costs them 𝟮𝟯 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 to get back into deep focus. Now imagine this happening 5-10 times a day. Your best engineers aren't shipping slow. We keep breaking their flow. It takes about 𝟭𝟱 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 of uninterrupted work to reach flow state. One notification can break it, even if the developer never opens it. Here is what helps: - 𝗗𝗲𝗳𝗮𝘂𝗹𝘁 𝘁𝗼 𝗮𝘀𝘆𝗻𝗰. Not everything needs an instant reply - 𝗕𝗹𝗼𝗰𝗸 𝗳𝗼𝗰𝘂𝘀 𝘁𝗶𝗺𝗲. Protect 2-4 hours for deep work daily - 𝗕𝗮𝘁𝗰𝗵 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. Combine five scattered pings into one message - 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝘀. If it's asked twice, write it down once One team I worked with blocked focus time for a month. Story completion went up 𝟯𝟱%. Bug reports dropped 𝟮𝟴%. Stop treating your team's focus like it's free. It's the most expensive resource you have. 👉 Learn more about it: https://lnkd.in/dwi2XbXG

🔥 We tracked one of our senior engineers for a week. Not surveillance. A voluntary experiment. He logged every interruption. Every context switch. Every "got a minute?" The results were painful. 📊 Monday through Friday: → 40 hours "at work" → 11 hours in meetings → 6 hours responding to Slack → 4 hours on code reviews → 2.5 hours on email and admin → Deep, uninterrupted coding: 8 hours. Total. For the week. 😤 8 hours of deep work in a 40-hour week. And we were paying him a senior engineer's salary to spend 80% of his time NOT engineering. 🤔 The math that changed our approach: Every context switch costs ~23 minutes to regain deep focus. He was switching context 7-8 times per day. That's 2.5+ hours of daily "reboot time" — pure waste. ✅ What we changed: → No-meeting mornings (9am-12pm = sacred deep work) → Slack goes async by default. Urgent = call. → Code reviews batched to 2pm-3pm → Questions collected and asked in batches, not one-by-one 📊 Result after 3 months: → Deep work went from 8 hours/week to 22 hours/week → Feature velocity nearly doubled → Engineer satisfaction went up (people like building things) Your best engineers don't need more hours. They need fewer interruptions. What would your team's deep-work-to-meeting ratio look like if you measured it? #Engineering #DeepWork #Productivity #CTO

You're bleeding 30% of your team's productivity to context switching. Every time developers rotate between different product areas, they lose momentum. They have to relearn the technical context, understand different user needs, and remember the strategic reasoning behind decisions. I've seen teams struggle with this - it's like asking a writer to switch between a romance novel, a technical manual, and a legal brief every few weeks. But this isn't just a coordination problem. It's an organizational design issue. Three PMs to seven engineers? In my experience working with companies of different sizes, that ratio creates more problems than it solves. Most teams I've worked with find success with dedicated developers focused on specific areas for extended periods - usually months to a year depending on the company stage. Here's what I recommend: start tracking your actual delivery times and cycle times. Document what happens when teams switch versus when they stay focused. I worked with one company that measured this and found their delivery predictability improved dramatically once they stopped the constant context switching. Present leadership with clear options: hire more developers or rescope the work. The data usually makes the choice obvious. Your developers will thank you for the consistency, and your delivery times will reflect the change. What's your experience been with shared development teams? PS: if you prefer to watch the full episode on the go, head over to Spotify or your favorite podcast app!

We've given AI agents the ability to reason, plan, and execute — but not the ability to remember across the tools we use every day. You spend an hour refining architecture in a chat session. You switch to your IDE and your assistant has zero context. You move to the CLI — starting from scratch again.  Today I'm super excited to share the Memory extension for VS Code.  It lets you capture decisions, code patterns, working context, and preferences as structured, cryptographically-signed memory — then carry it across any AI tool.  Every memory is:   - Content-addressed with BLAKE3   - Linked in a Merkle-DAG for tamper detection   - Signed with Ed25519 for provenance   - Verifiable on import, and agents can reject tampered memories  And because .pam files are plain JSON, you can commit them to Git alongside your code. Track how your project's AI memory evolves over time full version history, diffs, and audit trail through your existing Git workflow.  No more copy-pasting prompts. No more re-explaining your project. Your agent's context travels with your code — and its history lives in your repo.  Install it today: https://lnkd.in/g3pggrQS  Read the full blog for a deeper dive  #AI #AgenticWorkflows #VSCode #OpenSource #DeveloperProductivity #LLM #PortableAgentMemory

The AI mistake that's costing you 4.7 hours daily. I tracked 200+ knowledge workers for 30 days. 97% make the same productivity killer mistake. They're using AI like a Swiss Army knife instead of a surgeon's scalpel. The mistake? Context switching chaos. → Jump to ChatGPT for writing. → Switch to Claude for analysis. → Hop to Midjourney for images. → Back to ChatGPT for editing. Your brain burns 23 minutes refocusing after each switch. I discovered the batching method: Monday: Writing day (ChatGPT / Autoposting.ai only) Tuesday: Analysis day (Claude only) Wednesday: Visual day (Midjourney + Flux + Canva) Thursday: Strategy day (Perplexity only) Friday: Review day (DeepL + final tools) One tool. One purpose. One day. Before batching: → 8.3 hours for basic content creation → Mental exhaustion by 2 PM → 47% error rate from context juggling After batching: → 3.6 hours for same output → Energy until 6 PM → 12% error rate The psychology: Your brain craves single-tasking. AI amplifies focus, not chaos. Most people collect AI tools. Smart people master AI systems. What's your biggest AI context-switching trap?

Anthropic engineer: "You're not supposed to prompt Claude. You're supposed to build a system that prompts itself." One of the best workflows I've seen in a while. Most people use Claude like a smarter chat box. They write a prompt. They paste context. They explain the same constraints again. Then they wonder why the output drifts. But the better pattern is different: You do not prompt Claude once. You build a system that keeps prompting itself. That means: 1) Put the standing context in CLAUDE.md Your rules, repo structure, commands, architecture notes, taste, constraints. The boring stuff Claude should never have to ask twice. 2) Give it a repeatable loop Not: "Write code." Better: → inspect the repo → make a plan → change one thing → run tests → read the failure → patch again → summarize what changed That loop matters more than the first prompt. 3) Let tools carry the memory Git diffs. Test output. Linters. Logs. Docs. Previous runs. Claude gets much better when the environment tells it what happened instead of relying on vibes. 4) Treat prompts as scaffolding, not the product The prompt is not the workflow. The workflow is the harness around the model. That is the part most people miss. A good Claude setup is closer to an operating system than a chat conversation. The model reasons. The files hold memory. The tools create feedback. The loop forces correction. That is where the leverage is. I'm Shrey Shah Shah & I teach AI assisted coding and agents.

After 2,000+ hours using Claude Code across real production codebases, I can tell you the thing that separates reliable from unreliable isn't the model, the prompt, or even the task complexity. It's context management. About 80% of the coding agent failures I see trace back to poor context - either too much noise, the wrong information loaded at the wrong time, or context that's drifted from the actual state of the codebase. Even with a 1M token window, Chroma's research shows that performance degrades as context grows. More tokens is not always better. I built the WISC framework (inspired by Anthropic's research) to handle this systematically. Four strategy areas: W - Write (externalize your agent's memory) - Git log as long-term memory with standardized commit messages - Plan in one session, implement in a fresh one - Progress files and handoffs for cross-session state I - Isolate (keep your main context clean) - Subagents for research (90.2% improvement per Anthropic's data) - Scout pattern to preview docs before committing them to main context S - Select (just in time, not just in case) - Global rules (always loaded) - On-demand context for specific code areas - Skills with progressive disclosure - Prime commands for live codebase exploration C - Compress (only when you have to) - Handoffs for custom session summaries - /compact with targeted summarization instructions These work on any codebase. Not just greenfield side projects! I've applied this on enterprise codebases spanning multiple repositories, and the reliability improvement is consistent. I also just published a YouTube video going over the WISC framework in a lot more detail. Very value packed! Check it out here: https://lnkd.in/ggxxepik

Most teams use Claude Code without properly configuring it. So every new session starts with the same friction: Repeated explanations. Repeated conventions. Repeated corrections. Repeated mistakes. Configure these seven files once, and Claude Code can work with much more context from the start. 1. CLAUDE.md - Project context Define your stack, architecture, coding standards, and development practices. Use CLAUDE.local.md for machine-specific details. 2. .mcp.json- External integrations Connect tools like GitHub, Jira, Slack, databases, and other systems through MCP. 3. settings.json - Permissions and behavior Control tool access, model preferences, hooks, and developer-specific overrides. 4. rules/ - Modular conventions Keep coding, testing, and API standards in focused files instead of one large instruction document. 5. commands/ and skills/ - Reusable workflows Commands are explicitly invoked. Skills are loaded when relevant to the task. 6. agents/ - Specialized sub-agents Create focused agents for tasks like code review, security checks, or testing. 7. hooks/ - Automated guardrails Run validations, linting, formatting, or safety checks before and after tool actions. The idea is simple: Teach the agent how your team works once, and make that knowledge part of the repository. Less repetition. Fewer mistakes. More consistent workflows. Better AI-assisted development. How many of these seven are already in your repo? #ClaudeCode #AIEngineering #DeveloperProductivity #AgenticAI #SoftwareEngineering

🧵 Bioinformaticians: Drowning in multiple projects? Here's why context switching is killing your productivity—and how to fix it. 1/ Too many projects = too little progress. When your attention is scattered, your output drops. Context switching is the silent killer. 2/ Each project has its own biology: 🧬 Different TFs 🧪 Different cell lines ⚙️ Different pipelines Switching eats mental energy. 3/ Example: Jumping from CUT&RUN on H3K27me3 in mESCs to ChIP-seq on c-Myc in HeLa = full brain reboot. 4/ You spend time recalling: • QC parameters • Genome version • Where peaks are saved • Which scripts and tools you used 5/ And if the project isn't organized, you’ll waste even more time digging through old folders, scratch files, or rerunning commands. 6/ Solution 1: Ruthless prioritization Pick ONE project each day. Focus. Finish. Ship it. Half-done science doesn’t help anyone. Multitasking is a trap. 7/ Solution 2: Use consistent folder structures Example: project_name/ ├── raw_data/ ├── fastqc/ ├── trimmed/ ├── alignment/ ├── results/ ├── scripts/ 8/ Solution 3: Add a README to each project Include: • Data source • Goals • Key results • Analysis steps • TODOs This saves hours when switching context or collaborating. document as much as possible. it may seem to be a waste of time, but it saves you time long term 9/ Solution 4: Keep a running log Markdown or plain text. Example: ## 2025-04-10 - Trimmed reads with fastp - Aligned with bowtie2 (hg38) - Called peaks with macs2 10/ Key Takeaways: • Switching projects costs time and focus • Finish one before hopping to another • Structure + notes = less rework 11/ ✅ Action items: • Choose 1 project to focus on this week • Create folder structure & README • Start a daily or weekly log 12/ Deep work leads to deep insights. Prioritize. Organize. Write it down. Make future-you thank today-you. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter https://lnkd.in/erw83Svn

If you’re a backend engineer, you know this feeling. You’re in the zone, crushing it, and then … A notification comes across your screen. “Got a sec to hop on a quick call?” “Can you review my PR real quick?” “Just a quick question when you’re free…” Every ping pulls you out of your flow. And context switching? It’s brutal. Protect your focus. • Batch your communication windows. • Block off time on your calendar. • Set Slack to Do Not Disturb. Even 1–2 hours of uninterrupted focus can change your whole day. Illustration by: Ash Lamb