Handling Urgent Tasks

The AFLOW model advances agentic workflow optimization by treating it as a search space it explores with Monte Carlo analysis. One outcome is small models outperforming GPT 4o at 4.5% of the cost. There is vast scope for optimization of agentic workflow, due to the unlmiited potential combinations. This is likely to yield more progress than in underlying model development. The researchers are sharing code as well as a detailed paper (link in comments). Some highlights of the paper: 🌟 Enhanced Workflow Adaptability Through Operators. Operators such as "Generate," "Review & Revise," and "Ensemble" act as reusable workflow building blocks in AFLOW, enabling the framework to adapt efficiently to diverse tasks. This modular approach improves search efficiency and ensures robust solutions across six benchmark datasets, underscoring the value of integrating predefined patterns into automated systems. 🔄 Cost-Effective Task Execution with Model Agnosticism. Workflows generated by AFLOW allow less powerful models to outperform larger ones in cost-effectiveness, particularly in high-complexity tasks like GSM8K and MBPP. This scalability in performance at reduced computational costs makes AFLOW a game-changer for deploying AI in budget-sensitive applications. 📊 Iterative Improvement with Monte Carlo Tree Search. AFLOW’s tree-based structure retains successful experiences and avoids redundant failures, facilitating iterative improvements. For example, in GSM8K, AFLOW autonomously crafted workflows that performed similarly to manually designed structures, showcasing its ability to innovate through optimized search strategies. 🔍 Model-Specific Workflow Tailoring. Different language models require tailored workflows for optimal performance. AFLOW demonstrated that workflows optimized for one model (e.g., GPT-4o-mini) might not transfer perfectly to another (e.g., DeepSeek-V2.5), emphasizing the need for context-specific adaptations in AI systems. Code sharing of research such as this is an incredible amplifier of progress. Expect to see others take this excellent work further.

A reality check for any leader: Your crisis plan is only as strong as the person who goes first. Most teams assume a crisis will activate responsibility automatically. They think the plan will guide everyone. They expect people to step in at the right moment. But crises do not work that way. The real breakdown happens when every person waits for a signal that never comes. Hesitation does more damage than the crisis itself. A crisis-ready team runs on four behaviors: 1. Awareness → People notice early signs instead of assuming “it will pass.” Awareness stops problems before they spread. 2. Initiative → Someone steps forward even if it is not part of their role. Initiative prevents escalation. 3. Communication → Information moves quickly and clearly. No gaps, no guessing, no confusion. 4. Follow-through → What starts gets finished. No loose ends for the public to fill with their own narrative. Without these behaviors, plans stay on paper and the crisis gains control. With them, the team acts with clarity and the situation stabilizes before it spirals. Follow for weekly insights on crisis leadership and responsible communication.

If you’re building AI agents that need to work reliably in production, not just in demos, this is the full-stack setup I’ve found useful From routing to memory, planning to monitoring, here’s how the stack breaks down 👇 🧠 Agent Orchestration → Agent Router handles load balancing using consistent hashing, so tasks always go to the right agent → Task Planner uses HTN (Hierarchical Task Network) and MCTS to break big problems into smaller ones and optimize execution order → Memory Manager stores both episodic and semantic memory, with vector search to retrieve relevant past experiences → Tool Registry keeps track of what tools the agent can use and runs them in sandboxed environments with schema validation ⚙️ Agent Runtime → LLM Engine runs models with optimizations like FP8 quantization, speculative decoding (which speeds things up), and key-value caching → Function Calls are run asynchronously, with retry logic and schema validation to prevent invalid requests → Vector Store supports hybrid retrieval using ChromaDB and Qdrant, plus FAISS for fast similarity search → State Management lets agents recover from failures by saving checkpoints in Redis or S3 🧱 Infrastructure → Kubernetes auto-scales agents based on usage, including GPU-aware scheduling → Monitoring uses OpenTelemetry, Prometheus, and Grafana to track what agents are doing and detect anomalies → Message Queue (Kafka + Redis Streams) helps route tasks with prioritization and fallback handling → Storage uses PostgreSQL for metadata and S3 for storing large data, with encryption and backups enabled 🔁 Execution Flow Every agent follows this basic loop → Reason (analyze the context) → Act (use the right tool or function) → Observe (check the result) → Reflect (store it in memory for next time) Why this matters → Without a good memory system, agents forget everything between steps → Without planning, tasks get run in the wrong order, or not at all → Without proper observability, you can’t tell what’s working or why it failed → And without the right infrastructure, the whole thing breaks when usage scales If you’re building something similar, would love to hear how you’re thinking about memory, planning, or runtime optimization 〰️〰️〰️〰️ ♻️ Repost this so other AI Engineers can see it! 🔔Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources 📙I write long-form technical blogs on substack, if you'd like deeper dives: https://lnkd.in/dpBNr6Jg

How We Started Bug Management at takeUforward 🚀 In any budding startup, especially when you're building everything from scratch as a bunch of college students with no prior professional experience, managing user feedback and bugs can be chaotic. From day one, we gave our users the option to report bugs while using the website, ensuring a better user experience. But with a growing user base, we soon realized that managing all bug reports and feature requests in one place was becoming overwhelming. Each user had a unique request -> some system-specific bugs, others feature suggestions. As a service provider, resolving these issues was our responsibility because, at the end of the day, "customer obsession is the only trend that matters" (though, let’s be real, not every customer is meant to be obsessed with😃). 🫡 #TheChallenge: Efficiently Assigning & Resolving Bugs With a small team divided into problem setters, video editors, developers, editorial writers, and those handling miscellaneous tasks, we needed a seamless way to ensure reported issues reached the right person. Initially, we integrated ClickUp to automate task creation, assignments, and progress updates. But something was missing—users needed a direct way to communicate with us without the formality (and hassle) of email updates! 👾 #TheSolution: An Internal Bug Management Portal To streamline everything, I built an internal portal to: ✅ Monitor reported bugs across different sections ✅ Filter bugs based on priority & report time ✅ Track resolution time and last updates ✅ Allow direct communication with users via comments It took me a week of development time to build this (don’t judge the UI—it’s minimal and purely functional for internal use), but the impact has been huge: 🔹 Bugs in single digits: at any given time 🔹 Average resolution time: 48-60 hours 🔹 Every reported issue reaches us directly, and we respond firsthand This might seem like a basic operation from a developer's perspective, but for our team, it has transformed the way we handle user feedback. Small internal projects like these don’t just improve efficiency, they enhance user trust and satisfaction. Building things fast, iterating faster; that’s the startup way. 🚀

I've tried 100s of time management techniques.  This is by far my favourite: I used to work 80 hrs/week and call it "productive." When really I was: - Attending pointless meetings - Fighting countless small fires - Being involved in every decision Now I work less than 70% the time and get 4x as much done. The Eisenhower Matrix helped me get there.  It teaches you to categorise tasks by importance and urgency. Here's how it works: 1. Do It Now (Urgent + Important) Examples: - Finalise pitch deck before investor meeting tomorrow. - Fix website crash during peak customer traffic. - Respond to press interview request before deadline. Best Practices: - Attack these tasks first each morning with full focus. - Set a strict deadline so urgency fuels execution. 2. Schedule It (Important + Not Urgent) Examples: - Plan quarterly strategy session with leadership team. - Map long-term hiring plan for next 18 months. - Build a personal brand content system for LinkedIn. Best Practices: - Protect time blocks in advance. Never leave them floating. - Tie them to measurable outcomes, not vague intentions. 3. Delegate It (Urgent + Not Important) Examples: - Handle inbound customer service queries this week. - Organise travel logistics for upcoming conference. - Update CRM with latest sales call notes. Best Practices: - Build playbooks so your team executes without confusion. - Delegate with deadlines to avoid wasting time. 4. Eliminate It (Not Urgent + Not Important) Examples: - Tweak logo colour palette again for fun. - Attend generic networking events with no ICP fit. - Review endless “best productivity tools” articles. Best Practices: - Audit weekly. Cut anything that doesn’t compound long-term. - Replace low-value busywork with rest, thinking, or selling. If you are always reacting to what feels urgent,   You'll never focus on what matters. Attend to the tasks in quadrant 1 efficiently,  Then spend 60-70% of your time in quadrant 2.    That's work that actually builds your business. Which quadrant are you spending too much time in right now?  Drop your thoughts in the comments. My newsletter, Step By Step, breaks down more frameworks like this. It's designed to help you build smarter without burning out. 200k+ builders use it to develop better systems. Join them here:  https://lnkd.in/eUTCQTWb ♻️ Repost this to help other founders manage their time.  And follow Chris Donnelly for more on building and running businesses. 

I used to make software to help machine manufacturers manage their machines remotely. Twelve plus years ago I had a client that would roll out software updates to their technology kiosks. Even though they only had single digit thousands of devices, they did not push them out all at once. They pushed updates to their zip code, then their town, then their state, then their timezone, and then the whole US. Why did they follow this procedure even though they thoroughly tested the updates? Because if there was a software failure they wanted to limit the potential damage that their update would cause. They would "roll a truck" to fix the problem. They knew that selecting machines closer to headquarters would mean that they would have a lot smaller headache. Additionally, even if they bricked all of the local machines, the number of machines with problems would be measured with two or three digits and not four digits spread across the country. That is why the most shocking thing to me about the recent Crowdstrike issue is that they deployed to millions of devices all at once! From Crowdstrike on how they intend to prevent this from happening again: Refined Deployment Strategy ● Adopt a staggered deployment strategy, starting with a canary deployment to a small subset of systems before a further staged rollout. ● Enhance monitoring of sensor and system performance during the staggered content deployment to identify and mitigate issues promptly. ● Provide customers with greater control over the delivery of Rapid Response Content updates by allowing granular selection of when and where these updates are deployed. ● Provide notifications of content updates and timing. I am glad that they are taking this issue seriously but it seems crazy to me that an event like this had to happen for these type of changes. Message to everyone solution provider that makes an agent or every customer that uses an agent. A staggered rollout strategy should be absolutely required. Even if your company does not use Crowdstrike/Windows, you should be looking at all of your vendors that have an agent. What do you think? Are you going to take a look at agents as part of your vendor reviews? #fciso #crowdstrike

Land the plane. If you’re in it right now, dealing with a missed goal, a major bug, a failed launch, or an angry keystone customer, this is for you. In a crisis, panic and confusion spread fast. Everyone wants answers. The team needs clarity and direction. Without it, morale drops and execution stalls. This is when great operators step up. They cut through noise, anchor to facts, find leverage, and get to work. Your job is to reduce ambiguity, direct energy, and focus the team. Create tangible progress while others spin. Goal #1: Bring the plane down safely. Here’s how to lead through it. Right now: 1. Identify the root cause. Fast. Don’t start without knowing what broke. Fixing symptoms won’t fix the problem. You don’t have time to be wrong twice. 2. Define success. Then get clear on what’s sufficient. What gets us out of the crisis? What’s the minimum viable outcome that counts as a win? This isn’t the time for nice-to-haves. Don’t confuse triage with polish. 3. Align the team. Confusion kills speed. Be explicit about how we’ll operate: Who decides what. What pace we’ll move at. How we’ll know when we’re done Set the system to direct energy. 4. Get moving. Pull the people closest to the problem. Clarify the root cause. Identify priority one. Then go. Get a quick win on the board. Build momentum. Goal one is to complete priority one. That’s it. 5. Communicate like a quarterback Lead the offense. Make the calls. Own the outcome. Give the team confidence to execute without hesitation. Reduce latency. Get everyone in one thread or room. Set fast check-ins. Cover off-hours. Keep signal ahead of chaos. 6. Shrink the loop. Move to 1-day execution cycles. What did we try? What happened? What’s next? Short loops create momentum. Fast learning is fast winning. 7. Unblock the team (and prep the company to help). You are not a status collector. You are a momentum engine. Clear paths. Push decisions. Put partner teams on alert for support. Crises expose systems. And leaders. Your job is to land the plane. Once it’s down, figure out what failed, what needs to change, and how we move forward. Land the plane. Learn fast. Move forward. That’s how successful operators lead through it.

Design tools once required years to master. Today, creating premium designs takes one prompt. OpenAI's latest image generator reached 1M users in one hour, a milestone that took ChatGPT five days. Yet, this isn't just about design or OpenAI. The question that came to my mind was: What happens to careers and businesses when skill acquisition reduces from years to minutes? Three shifts I see changing the rules of the game: 1. The Collapse of Adaptation Sequences Technology adoption traditionally followed phases: → Innovators experiment → Early adopters explore → Mainstream integrates → Institutions adapt Now these phases collapse: → Adoption compresses from years to weeks → Large institutions struggle to keep pace → Companies must navigate all phases simultaneously 2. Inversion of Strategic Priorities → Yesterday: Analyze, optimize, adapt gradually → Today: Best practices become tomorrow's liabilities → Tomorrow: Adaptation speed outperforms efficiency 3. The AI Arbitrage Opportunity → AI scales exponentially; expertise grows linearly → Bridging these domains unlocks disproportionate value → Winners combine industry knowledge with AI Organizations now exist in two different timelines: → Traditional Time: Quarterly plans, annual budgets → Acceleration Time: Weekly pivots, daily experiments The competitive gap between these two worlds grows exponentially. Companies unable to adapt to acceleration time will fall irreversibly behind. Success in this reality requires: → Shifting from execution to orchestration → Recognizing distribution as your strongest moat → Prioritizing adaptation speed over operational efficiency Most companies and individuals are still playing by old rules in a game that no longer exists. The greatest risk I see isn't resistance to change. It's incremental adaptation in an exponential world.

I used to think working under pressure meant pushing myself until I burned out until I learned this one truth: Pressure isn’t the problem. How we manage our energy under pressure is what decides whether we grow or collapse. Here’s what helped me work under pressure without burning out: 📌 I stopped treating every task like an emergency Not everything is “urgent.” When I ranked tasks based on actual impact rather than fear, the panic dropped and clarity rose. 📌 I switched from time management to energy management I do mentally heavy tasks when I’m mentally fresh, creative work when I feel relaxed, and routine tasks when my energy dips. Same hours, but better outcomes. 📌 I built “micro-breaks” into my day 2 minutes of stretching, stepping away from the screen, or deep breathing resets the brain. Pressure doesn’t kill you but continuous cognitive strain does. 📌 I separated performance from self-worth I stopped attaching my identity to how much I get done. It made me work smarter instead of emotionally reacting to stress. 📌 I ask myself one question every time I feel overwhelmed: “Is this pressure pushing me to grow or pushing me to break?” If it’s the second one then I step back, delegate, or slow down. Working under pressure isn’t about being “tough.” It’s about protecting your mind while delivering your best. If you’re someone who works hard, please remember: You don’t have to burn yourself to prove your worth. 💙 Do you agree?