DEV Community: Alex (EngLead AI) 🤖

Prime Agent: an open source coding and research agent for long-running work

Alex (EngLead AI) 🤖 — Mon, 09 Nov 2026 09:01:25 +0000

Prime Agent (Prime Intellect)

The agent that writes code should not be the one that judges it, and Prime Agent is built around that rule. It is Prime Intellect's open source coding and research agent for long running work, now rebuilt in Rust. Its core idea is the Recursive Language Model: context is treated as variables and subagents are called like functions inside a persistent Python REPL. A Continual Harness then stores memories, skills, prompts, and subagent specs as durable state that the agent can refine in small, evidence backed updates. The stable release is v0.10.2 from 28 October 2026, and the repo now has 22,632 stars. Prime Intellect says more than 2,000 agents carried out the Rust rewrite.

What you get

A reviewer separate from the writer, so the agent that writes the code is not the one judging it
Durable state across sessions: memories, skills, prompts and subagent specs persist in the Continual Harness
Subagents called like functions, with context treated as variables inside a persistent Python REPL
A Rust rebuild for long running work, at stable release v0.10.2 and MIT licensed per the README

Try it on a long running task first, and keep the review step separate from the writing step.

Repo: https://github.com/PrimeIntellect-ai/prime-agent

Quick poll

What would you use Prime Agent (Prime Intellect) for first?

Long-running coding work
Research tasks
The subagent-as-function approach
Watching it for now

Vote in the comments.

If you found Prime Agent (Prime Intellect) interesting, I write about tools like this every week in The AI-Native Engineering Leader, what actually works for engineering leaders building with AI agents, and what to skip.

WeKnora: turn scattered documents into one queryable knowledge system

Alex (EngLead AI) 🤖 — Sun, 08 Nov 2026 09:01:03 +0000

WeKnora

Most teams do not have a knowledge problem. They have a scattered documents problem, and every answer starts with another search. WeKnora, from Tencent, turns raw documents into one system you can query three ways: fast RAG answers with citations, a multi-step agent that can use tools and skills, and a wiki that organises the same knowledge into linked pages. A new upload merges into existing pages, and deleting a document rewrites or removes the pages that cited it. Version 0.9.0 arrived on 29 October 2026 with an in-app assistant, multi-agent collaboration, and automations. It now has 36,779 stars on GitHub and runs self-hosted with Docker, or as a single Lite binary.

What you get

Three ways to query the same knowledge: cited RAG answers, a tool-using agent, and a linked wiki
Knowledge that stays consistent: uploads merge into existing pages, deletions rewrite or remove the pages that cited them
Self-hosted with Docker, or as a single Lite binary
Version 0.9.0 adds an in-app assistant, multi-agent collaboration, and automations

Start with the documents your team already searches for, and let the wiki grow from what people actually ask.

Repo: https://github.com/Tencent/WeKnora

Quick poll

What would you use WeKnora for first?

Fast RAG answers with citations
The multi-step agent
The auto-built wiki
Self-hosting it with Docker

Vote in the comments.

If you found WeKnora interesting, I write about tools like this every week in The AI-Native Engineering Leader, what actually works for engineering leaders building with AI agents, and what to skip.

TrueForge: an agent harness for running agents as a team

Alex (EngLead AI) 🤖 — Sun, 08 Nov 2026 08:18:35 +0000

TrueForge

Running an agent for a team is the hard part, and that is what TrueForge is built for. It is an open source agent harness from TrueFoundry that runs the full execution loop for you: model calls, MCP tools, skills, sandboxing, approvals, context, and session state. Colleagues get a chat window, your app gets an HTTP API with a TypeScript SDK, and you can embed the UI in your own product. It works with OpenAI, Anthropic, and Gemini, and runs locally with SQLite or hosted with Postgres and Redis. It launched on 19 August 2026 and now has 6,449 stars on GitHub. The latest release, version 0.184.2, shipped on 6 November 2026 under the MIT license.

Repo: https://github.com/truefoundry/trueforge

Quick poll

What would you use TrueForge for first?

Trying it on a real project
Reading the docs first
Watching it for now
Skipping it for now

Vote in the comments.

If you found TrueForge interesting, I write about tools like this every week in The AI-Native Engineering Leader, what actually works for engineering leaders building with AI agents, and what to skip.

Moli: a headless browser built for agents

Alex (EngLead AI) 🤖 — Sat, 07 Nov 2026 11:38:28 +0000

Moli: a headless browser built for agents

Most agents do not need a full browser. They need the page structure and the result of a script.

Moli is a headless browser built from scratch in Rust for that job. It keeps the DOM and computed CSS as the source of truth, and runs layout or paint only when a task genuinely needs it, so extracting HTML or Markdown never triggers a render.

What you get

V8 JavaScript, Fetch, WebCrypto, and storage
CLI, CDP, WebDriver, and MCP access
34 releases and 22,888 stars on GitHub as of 7 November 2026

Try it first for reading, and test it on your own pages before trusting it with clicking.

Repo: https://github.com/lexmount/moli

Quick poll

What would you use Moli for first?

Reading and extracting pages
Running scripts headlessly
Driving it through MCP or CDP
I would not trust it with clicking yet

Vote in the comments.

If you found Moli interesting, I write about tools like this every week in The AI-Native Engineering Leader, what actually works for engineering leaders building with AI agents, and what to skip.

Cybersecurity in the AI Era

Alex (EngLead AI) 🤖 — Fri, 06 Nov 2026 13:01:21 +0000

Scams are becoming more convincing. AI tools are gaining more access. A practical guide to trust, access and accountability for engineering leaders.

This is a short version of the full article on EngLead AI. Read the full story here: https://engleadai.substack.com/p/cybersecurity-in-the-ai-era

An assistant starts by helping your engineers find documentation. A few useful integrations later, it can search repositories, read incident notes and send replies to suppliers.

Each addition has a reasonable business case.

Who reviewed what those capabilities allow it to do together?

Consider a supplier request. An email describes a failing integration, names the right systems and asks for a diagnostic report. An engineer passes it to the assistant. The assistant finds relevant material and prepares a convincing reply.

The sender is an impersonator. The draft includes internal incident information that should never leave the company.

The engineer may be experienced. The assistant may have answered accurately. The failure sits in the workflow connecting a believable request, sensitive information and the ability to send it somewhere.

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A familiar interaction can open the door

There is a documented example of recruitment being used as part of a major intrusion. In a December 2024 announcement, US and Japanese authorities described an attacker posing as a recruiter on LinkedIn. A Ginco employee received a supposed pre-employment test linked to malicious Python code and was subsequently compromised. Authorities linked the campaign to the theft of cryptocurrency worth $308 million from DMM Bitcoin in May 2024.

That announcement does not establish AI involvement. It shows how an ordinary professional interaction can become an entry point.

AI adds ways to make those interactions more convincing. The FBI advisory on generative AI fraud describes criminals using generated text, images, voices and video to support deception and reach more people.

A polished message, familiar voice or plausible professional profile therefore needs to be assessed in the context of what the person is asking you to do.

Connected assistants give a request greater reach

An AI model can propose an action. The surrounding application supplies the tools, credentials and permissions that make the action possible. Reading an incident report, preparing an email and sending it are separate capabilities, even when the interface makes them feel like one conversation.

OWASP describes excessive agency in terms of excessive functionality, permissions and autonomy. That is a useful lens for reviewing an assistant that keeps gaining integrations.

There are also different ways a harmful request can travel through the workflow. An impersonator may persuade a person to approve something. Malicious instructions in a document or webpage may redirect an assistant beyond the user's intended task. That second mechanism is prompt injection. The NCSC explains why application design must limit its consequences.

These problems can interact. They require different controls.

Three decisions deserve leadership attention

What establishes trust? Decide how consequential requests are verified, especially when they involve new recipients, access changes, software installation or sensitive information.

What authority does the workflow need? Make its ability to read, change and disclose information visible. Useful access should have a clear purpose and an accountable owner.

What happens when something looks wrong? Give the team a way to pause the workflow, understand what happened and recover. That capability needs to exist before the assistant becomes part of everyday operations.

These decisions also make adoption easier to explain. People can move faster when they understand which actions are supported, which need review and where to ask for help.

What the full article covers

The supplier assistant offers a practical starting point. It should help an engineer answer a legitimate question using appropriate information, with a clear decision before anything is sent externally.

The full article on EngLead AI is the implementation playbook for building that workflow. It covers:

Defining the workflow before choosing controls, with a workflow register you can actually maintain
Putting controls where trust becomes access: independent verification, separating user authority from app identity, retrieval controls, narrow tools, and approval specific enough to enforce
Proving that the controls work, with 13 acceptance tests
Making intervention part of the design: audit trail, detections, and a stop and recovery rehearsal
Rolling out in stages over four weeks, and measuring the value

By the end, you will have the material for four useful deliverables: a workflow register, an enforceable permissions design, a release test suite and an operating runbook.

Read the full story here: https://engleadai.substack.com/p/cybersecurity-in-the-ai-era

Subscribe to EngLead AI for the complete implementation playbook and practical frameworks to bring into your next design review: https://engleadai.substack.com/subscribe

Writing The AI-Native Engineering Leader: on combining AI with existing technology to manage and operate teams at scale.

Subscribe at read.englead.ai for the weekly issue.

Join Me

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EM Operating System: Your Brain Is a Terrible Database

Alex (EngLead AI) 🤖 — Sun, 19 Apr 2026 14:06:47 +0000

Monday, 8:47 a.m.

Seven Slack DMs you haven't opened. Three 1:1s stacked before lunch. A half-written planning doc from Friday. A decision from last quarter you need to remember, buried in a thread you can no longer search. A promotion packet open in three browser tabs. Two incident follow-ups you said you'd reply to "first thing Monday."

It's 8:47 a.m.

You have not had coffee.

This is the job. The job isn't writing code. It isn't even running standups. The engineering manager's actual job is carrying context between decisions without losing it. And your brain, with respect, is a terrible database.

🌀 Read Issue 001 here: https://read.englead.ai/p/em-operating-system-ai-native

What I built

I built a Second Brain for this. I call it The EM Operating System. It took one Saturday afternoon. It runs local, costs nothing beyond a Google account, and lives on top of four things you can install with five terminal commands.

The four pieces:

1. Obsidian

Obsidian is a plain markdown editor that treats your notes as files in a folder on your laptop. Not a SaaS. Not a database. Just files.

Why it matters for an EM: everything you write about work (1:1 notes, architecture decisions, hiring feedback, retro threads) is permanently yours. Obsidian shuts down tomorrow? Your notes still open in any text editor. That reversibility is why senior engineers can put real work context into it without worrying about vendor risk.

It also renders markdown beautifully, supports backlinks between notes, and has a mobile app. Free for personal use.

2. Claude Code

Claude Code is an AI agent that runs in your terminal and reads the files in the directory you point it at. Not a chat app. Not a sidebar. An agent that writes, edits, organises, and links files on your behalf.

Why it matters for an EM: you point Claude at your ~/brain folder once. From then on, every capture you throw at it (a voice memo transcript, a Slack screenshot, three sentences from a 1:1) gets filed in the right place, with frontmatter, with backlinks to the people and projects it touches. The filing stops being your job.

The unlock isn't "AI writes my emails." The unlock is "AI carries the connective tissue between my 1:1s, roadmap reviews, incident notes, and hiring loops, so I don't drop threads."

3. Google Drive + rclone

rclone is a command-line tool that syncs any folder to any cloud storage. Install it once. Point it at your ~/brain folder. It now syncs to Google Drive every 15 minutes.

Why it matters for an EM: you work on a laptop at the office, a different laptop at home, a phone on the commute. Obsidian Sync costs $10/month. Dropbox and iCloud both have weird edge cases with Obsidian's file locks. rclone + Google Drive is free, reliable, and uses storage you already have.

And because Obsidian operates on files, not a proprietary cloud database, rclone sync is a real-time replica. Not a backup. A working mirror.

4. PARA

PARA is a folder method from Tiago Forte: Projects / Areas / Resources / Archive. Four folders. Everything filed into exactly one of them.

Why it matters for an EM: it's action-first, not archive-first. Projects have a finish line. Areas are your ongoing responsibilities (the team, the role, 1:1s with your manager). Resources are reference material. Archive is done-or-abandoned. Anything else goes in a fifth folder, Inbox, and gets processed weekly.

No tag taxonomy. No nested subfolders. No database schema to memorise. Five folders, one rule: new things land in Inbox; old things move out.

~/brain
├── 0 - Inbox       ← anything you capture ends up here first
├── 1 - Projects    ← active work with a finish line
├── 2 - Areas       ← ongoing standards (the team, the role)
├── 3 - Resources   ← reference material, people, captures
└── 4 - Archive     ← done, paused, or moved on

How it actually runs on a Monday

Back to 8:47 a.m. Your report mentions a decision from two weeks ago in the opening minute of your 1:1.

Old world: you apologise, open five Slack search tabs, scroll through a thread, fail to find it, promise to circle back.

New world: you type a single line into Claude Code. "What did we decide about the retry policy on payments-service in the last three weeks?" It reads your vault, finds the ADR, finds the 1:1 note where the decision was referenced, finds the Slack screenshot you captured, and gives you the summary plus links. Thirty seconds. You're back in the conversation.

Same capability, different layer. The decision was always there. You just stopped being the index.

Why "operating system"

I call it an operating system because that's what it is. Not an app. Not a note-taking tool. An opinionated stack of primitives that together run the EM's knowledge work the way the OS on your laptop runs your applications.

Obsidian is the filesystem.
Claude Code is the process scheduler (it decides which files to read, write, and link based on what you ask).
rclone + Google Drive is the network layer.
PARA is the directory structure.

You already understand operating systems. This one just runs on your knowledge work instead of your apps.

The full setup

I wrote Issue 001 of EngLead AI as the complete walkthrough: every install command, every copy-paste prompt, the rclone sync config, the para-capture Claude Code skill that turns the agent into your personal librarian, and the Obsidian settings that make it all feel native.

About 45 minutes end to end. Zero prior experience with Obsidian, PARA, or Claude Code required. Mac only for now.

🌀 Read Issue 001 here: https://read.englead.ai/p/em-operating-system-ai-native

Writing The AI-Native Engineering Leader: on combining AI with existing technology to manage and operate teams at scale.

Subscribe at read.englead.ai for the weekly issue.

🤔 How are you building your personal brand?

Alex (EngLead AI) 🤖 — Sat, 14 Mar 2026 17:33:36 +0000

I've been thinking a lot about this lately. Some people have a full content strategy, scheduling tools, and a weekly rhythm. Others post when inspiration strikes. And plenty of smart people avoid it entirely.

Anonymous Survey 👉 https://forms.gle/Yxth9myeRp5Wy3Gx6

There's no wrong answer — but I'm genuinely curious where you fall.

I want to understand how professionals are really approaching personal branding in 2026:

🔧 What tools are you actually using (vs. what you've tried and dropped)?
⏰ How much time are you investing per week?
💰 Are you spending money on it — or going DIY?
🚧 What's the biggest thing holding you back?

I'll put together a write-up of what I find and share it here — figured we could all learn something from how others are approaching this.

Here's what I suspect:
✅ Consistency beats perfection
✅ Most tools get abandoned within 3 months
✅ "I don't have time" usually means "I'm not sure it's worth it"

Am I wrong? Anonymous Survey 👉 https://forms.gle/Yxth9myeRp5Wy3Gx6

The FAANG is dead💀

Alex (EngLead AI) 🤖 — Fri, 09 Jan 2026 19:30:43 +0000

Remember when landing a job at Facebook, Amazon, Apple, Netflix, or Google meant you'd made it? When FAANG was the career destination, the undisputed kings of tech?

Those days feel like a lifetime ago.

The acronym itself is showing its age. Facebook rebranded to Meta three years ago, betting the company on a metaverse that never quite materialized. Netflix went from disrupting television to being disrupted, facing fierce competition from every entertainment conglomerate with a streaming app. And the others? They're still massive, still profitable, but the mystique has faded.

Meanwhile, the new power players don't fit neatly into a catchy acronym. OpenAI is reshaping how we think about technology itself. Nvidia's market cap has exploded past most FAANG members. Anthropic, xAI, and a dozen other AI startups are attracting the talent that once flocked exclusively to Silicon Valley's established giants.

The mass layoffs didn't help either. Tens of thousands of engineers who thought they had job security discovered that even FAANG bleeds. The promise of stability evaporated, and with it, part of the allure.

But here's the question: Is FAANG actually dead, or has it just evolved into something different? Are we witnessing the end of an era, or just the tech industry doing what it does best—constantly reinventing itself?

What do you think? Is FAANG still the gold standard, or are we living in a post-FAANG world?

Vote

No: ❤️
Yes: 🔥
Never: 🦄

💡Only 20% of Developers are Happy at Work?

Alex (EngLead AI) 🤖 — Wed, 20 Nov 2024 17:03:23 +0000

Within Meta, we conduct a bi-annual survey called Pulse. It's our internal tool for gathering employee feedback about their experiences, focusing on three key dimensions: Manager, Company, and Personal Experience. The survey helps us measure employee and developer happiness, signal team health, and guide our leaders in improving Meta's work environment.

The recently released Stack Overflow 2024 Developer Survey provides a fascinating broader perspective that complements our internal insights. Let's break down the key findings that every tech leader and engineer should know.

Several factors contribute to this widespread unhappiness:

Technical Debt: 62% of developers cite technical debt as a primary source of frustration, indicating that working with outdated or inefficient codebases hampers their productivity and morale. [Stack Overflow Blog]
Salary Declines: Many developers have experienced salary reductions over the past year. For instance, the median salary for PHP developers has decreased to $49,000 annually, reflecting broader economic challenges within the tech industry. [Shift Magazine]
Workplace Pressures: High-pressure environments, characterized by tight deadlines and excessive workloads, contribute to burnout and job dissatisfaction among developers. [Shift Magazine]

The survey also highlights some fascinating trends:

Flexibility > Salary: Developers continue to value workplace flexibility even more than compensation.
Role-Specific Joy: Engineering managers find fulfilment in driving strategy, while embedded developers love improving hardware quality.

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📊 Only 1 in 5 Individual Contributors are happy at work

Individual Contributors

19.3% are Happy at Work
47.7% are Complacent
33% are Unhappy

People Managers

25.9% are Happy at Work
47.8% are Complacent
26.3% are Unhappy

Across both groups, complacency is alarmingly high, and genuine job satisfaction remains rare. Although people managers report slightly higher happiness levels, these figures reveal systemic workplace culture, expectations, and support issues.

What factors contribute to this dissatisfaction?

📌 Technical debt and outdated processes

📌 Burnout from high-pressure environments

📌 Limited career growth or unclear advancement paths

📌 Misalignment between developers' values and organizational goals

📊 Top Industries by Developer Employment

Software Development: 41.3% 💻
Fintech: 5.7% 💳
Internet/Telecom: 5.6% 🌐
Banking/Financial Services: 4.8% 🏦
Healthcare: 4.4% ⚕️
Manufacturing: 4.4% 🏭
Retail & Consumer Services: 4.4% 🛍️

What does this tell us?

📌 The Traditional-Digital Divide: While pure software companies still dominate, nearly 60% of developers work in traditional industries that have transformed into tech companies. This trend reinforces the idea that every company is becoming a tech company.

📌 Financial Sector's Influence: Fintech and Traditional Banking employ over 10% of developers. Throughout my career working with financial institutions, I've witnessed their evolution from tech consumers to innovators.

📌 Essential Services Transformation: Healthcare, Manufacturing, and Retail each claim 4.4% of the developer workforce---highlighting how digital transformation reshapes core industries. These sectors have progressed beyond merely adopting technology; they're now driving innovation.

📊 Factors Contributing to job satisfaction

Happy points

Top factor: "Improving quality of code and developer environments" leads, with a group mean satisfaction of 20.5 and total mean satisfaction of 21.1.
Second-highest factor: "Learning/using new tech" scores 17.8 group mean and 18.8 total mean satisfaction.
Building environments/architecture ranks third, at 16.5 (group) and 16.3 (total).
Driving strategy for my team scores 15.7 (group) and 14.1 (total).
Lowest factors: "Securing data," "Observability," and "Open Source" remain under 5 points, showing limited impact on satisfaction.

Unhappy points

Improving quality of code and developer environments is also the top factor for dissatisfied developers, scoring 21.3 (group mean) and 21.1 (total)---highlighting its universal importance.
Learning/using new tech is next, with 19.4 (group) and 18.8 (total).
Building environments/architecture maintains relevance at 16 (group) and 16.3 (total).
Driving strategy for my team is less prioritized among unhappy developers (12.7 group mean), while other areas like "being a power user" and "working with new hardware" score higher.

Key Takeaways

📌 Universal importance: Both happy and unhappy developers strongly prioritize "Improving code quality" and "Learning new tech."

📌 Role-specific differences: Happy developers score "Driving strategy for my team" higher than unhappy ones, indicating this factor's unique motivational impact.

📌 Underestimated factors: Contributions to open source and securing environments, while less impactful overall, might serve as niche drivers for specific developer groups.

📌 Strategic alignment: Opportunities to improve code and explore new technology are vital to boosting satisfaction universally.

The Role of Salary in Satisfaction

Salary remains critical in job satisfaction, but its impact varies significantly by country and context. As Erin Yepis from the Stack Overflow team explained in the "Dev Interrupted" podcast:

"I looked at the top 10 countries that responded to the developer survey. Most happy developers are definitely above the 50th salary percentile, but the relationship between salary and satisfaction is stronger in some countries than others. For example, job satisfaction scores stayed consistent in the Netherlands regardless of salary. However, developers in Brazil or France were more swayed by higher salaries." -- Erin Yepis

This highlights how context matters. Developers' satisfaction may be less tied to income in countries with more robust social safety nets or more balanced work cultures.

Why Satisfaction Matters for Leaders

As a leader, these insights hit home. Whether you're managing a team of engineers or leading at the senior leadership (CxO, VP) level, this data is a call to action:

Invest in your team's growth: Developers value learning opportunities and using new technology. Providing these opportunities keeps teams engaged and excited.
Focus on impact: Improving code quality and enabling developers to build meaningful solutions drives satisfaction at all levels.
Recognize role-specific motivations: Engineering Managers derive joy from driving strategy, while embedded developers thrive on high-quality hardware. Understanding these nuances helps create tailored environments for success.
Foster flexibility: Post-COVID, flexibility remains more important than ever. Hybrid and remote working options consistently rank as top motivators, even above salary in many cases.

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💬 Your turn

What drives satisfaction in your role?
What strategies have helped you create happier, more engaged teams if you're a leader?
Let's start a conversation and learn from each other.

P.S. If you're interested in shaping Meta's engineering culture, feel free to DM me.

[Data: Stack Overflow Developer Survey 2024]

Celebrating 10K Followers: Our Journey Together 🎉

Alex (EngLead AI) 🤖 — Sat, 09 Nov 2024 12:01:30 +0000

Hello, wonderful dev.to community!

Alex (EngLead AI) 🤖Follow

Technical Entrepreneur writing about high-performing teams, building healthy engineering culture, and scaling teams. @Meta / ex-HSBC || CareerHub - https://discord.gg/AjVbnWKYbJ

I am absolutely thrilled to announce that we've reached a significant milestone - 10,000 followers! When I began sharing my experiences and insights on dev.to, I could never have imagined that so many fellow engineers, tech enthusiasts, and aspiring developers would join me on this journey.

A Heartfelt Thank You

First and foremost, thank you. Your support, encouragement, and engagement have been the driving force behind every article I've written. Whether you've been here since my first post or joined along the way, your presence and participation mean the world to me.

A Look Back at Our Most Popular Articles

Over the past months, we've explored a variety of topics together. Here are some of the articles that resonated most with you:

"14 Case Studies: Master System Design in a Month"
- Published: Sept 19
- An in-depth guide to mastering system design through real-world case studies.
"Amazon Software Engineer Levels: Roles and Expectations [With Salary]"
- Published: Jul 8
- A comprehensive look at the career progression and compensation at Amazon.
"Meta (Facebook): Software Engineer Levels"
- Published: Jan 25
- An exploration of software engineering roles and growth at Meta.
"🚀 Roadmap: From Junior Software Engineer to CTO in 1 Year"
- Published: Apr 1
- A strategic guide for rapid career advancement in the tech industry.
"Google Software Engineer Levels: Roles and Expectations [With Salary]"
- Published: May 21
- Insights into the roles, expectations, and compensation at Google.
"12 Tips for Acing MAANG Software Engineering Interviews"
- Published: Mar 10
- Practical advice to help you succeed in interviews at major tech companies.
"🧠 4 Traits of Successful Engineers"
- Published: Nov 12
- An exploration of the key characteristics that contribute to engineering success.
"Level Up Your Coding Interview Skills: 5 Secret Weapons"
- Published: Oct 3
- Tools and techniques to enhance your coding interview performance.
"🗺️ Crack the System Design Interview @ FAANG (Template)"
- Published: Nov 25
- A template to help you navigate system design interviews at top tech firms.

Your Engagement Makes the Difference

It has been incredibly rewarding to see how these articles have sparked discussions, aided in your career growth, and even inspired some of you to start writing your own posts. Your comments, messages, and shared experiences have enriched our community and fostered a collaborative learning environment.

Celebrating You: the Heart of Our Community

This milestone isn't just about numbers; it's about us - a collective of curious minds passionate about technology and growth. Each of you brings unique perspectives and insights, making our interactions vibrant and meaningful.

Looking Ahead: Exciting Content on the Horizon

Your enthusiasm fuels my passion for sharing knowledge. Here's a glimpse of what's coming up:

In-Depth Tutorials on cutting-edge technologies.
Career Development Series to help you navigate the tech industry.
Interactive Q&A Sessions where we can engage in real-time discussions.

Join Me

Read more about Engineering Culture in MAANG (Big Tech), ⚡Newsletter, Twitter and LinkedIn for more Career, Leadership and Growth advice.

Let's Continue the Journey Together

I invite you to keep sharing your thoughts, feedback, and suggestions. If there's a topic you're eager to explore or a question you'd like answered, please don't hesitate to reach out.

Once Again, Thank You!

Reaching 10K followers is a shared achievement, and I couldn't have done it without you. Here's to many more articles, conversations, and milestones together!

Stay curious, keep coding, and let's continue to grow together.

Alex (EngLead AI) 🤖Follow

Technical Entrepreneur writing about high-performing teams, building healthy engineering culture, and scaling teams. @Meta / ex-HSBC || CareerHub - https://discord.gg/AjVbnWKYbJ

What Does It Take to Build High-Performing Teams?

Alex (EngLead AI) 🤖 — Wed, 16 Oct 2024 21:27:03 +0000

Some might say the key to building high-performing teams lies in setting SMART goals, establishing a clear vision, and crafting a multi-year strategy. Others might argue it’s about defining the right team charter, establishing stretch goals, or providing continuous feedback and development opportunities to push people forward. While these are undoubtedly essential elements, they are not the core of what truly makes a team excel.

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The reality is much simpler. What we truly need are two things: Integrity and Being Human.

Let me tell you a story.

When I worked for a small startup called DueDil, we faced a significant challenge: building and deploying a new API for all our customers, who were financial institutions with large and complex businesses. This multi-half program required careful planning, execution, migration, and deployment plans to avoid production incidents for our customers.

Our team was small—just four software engineers. But what we lacked in numbers, we made up for in trust and dedication. On deployment day, we were ready with plans A, B, and even C. We had rollback strategies, deployment strategies, and metrics in place. This wasn’t just a team; it was a dream team. It also proved that you don’t need 20 people to create a real business and customer impact. What you need is trust.

As we reached the culmination moment and pressed the button to switch all customers to the new system, we kept our eyes glued to the dashboards. As with any major production deployment, we hit a snag. Problems started unravelling quickly, but our meticulous planning paid off. We managed to get the situation under control by rolling back one of the batches, patching the Redis issues, and continuing forward with the rest of the release.

In hindsight, it was a slight hiccup with no real impact. We handled the situation well, and by the end of the day, all customers were on the new API—more scalable, more performant, and a significant win for the business. This API became one of the core products, generating 40% of the company’s global revenue.

But what made this experience truly special wasn’t just the technical success — the people sitting together and sticking through this process. We had a clear execution plan, knew our roles and responsibilities, and were selfless. We were united in our goal to do good, deliver, and create value, not for ourselves but for others.

Once all was done, one team member took out a guitar, another produced a bottle of wine, and we celebrated. It was a moment of reflection that felt special, bringing us closer together.

This story illustrates the power of Integrity and Being Human in building high-performing teams.

In our case, integrity meant staying true to our commitment to deliver a quality product, even when faced with challenges. It meant being honest about our problems and working together to solve them. Integrity in a team setting looks like this:

Transparent communication, even when things go wrong
Consistency between words and actions
Accountability at all levels
Ethical decision-making, prioritizing long-term success over quick fixes

When team members operate with integrity, it creates a foundation of trust that allows for more open collaboration, innovative problem-solving, and resilience in the face of challenges.

Creating Trust: The Heart of Connection

Our celebration after the deployment wasn’t just about the technical win and recognizing our shared trust. Creating trust in a team means:

Acknowledging that we’re more than just our roles or job titles
Celebrating successes together
Supporting each other through challenges
Recognizing individual contributions and strengths
Creating space for personal connections beyond work

When team members feel seen and valued as whole people, they’re more likely to bring their full selves to work, leading to increased creativity, engagement, and commitment to shared goals.

The Synergy of Integrity and Trust

When integrity and humanity are combined, you create an environment where people feel safe taking risks, sharing ideas, and collaborating openly. High performance becomes a natural outcome rather than a forced goal.

In this atmosphere:

Conflicts are addressed constructively because there’s mutual respect and trust.
Innovation thrives as people feel comfortable sharing unconventional ideas without fear of ridicule or rejection.
Resilience increases because the team supports each other through challenges, knowing they have a safety net of understanding and respect.
Retention improves as team members feel valued and connected to their work and colleagues, leading to long-term commitment and loyalty.

In Big Tech, the emphasis often lies on outcomes, business impact, and results, sometimes at the expense of the individual. Company culture can disregard the person behind the work, pulling us into a reality where we forget to be human and lose our integrity. But these are the very foundations that underpin success for any team, group, or organization.

The Reality of Big Tech vs. True Team Success

In Big Tech, the emphasis is often on outcomes, business impact, and results. However, company culture can sometimes disregard the person and the individual. Being pulled into that reality, we sometimes forget about being human and lose our integrity. However, these are precisely the underpinnings of success for any group, team, or organization.

Our experience at DueDil shows remarkable things can happen when you prioritize trust, integrity, and trust. A small team of four can achieve what larger teams might struggle with, creating significant business impact and forming bonds beyond the workplace.

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Conclusion

In conclusion, building high-performing teams isn’t about complex frameworks or pushing people to their limits. It’s about creating an environment where people trust each other implicitly, integrity is non-negotiable, and achievements are celebrated. The goals, strategies, and performance naturally follow when these elements are in place. When people feel truly seen, trusted, and connected, there’s no limit to what they can achieve together.

So, if you want to build a high-performing team, start by fostering integrity and trust. The rest will follow, and you might find yourself with a dream team that delivers results, creates lasting memories, and impacts far beyond the bottom line.

14 Case Studies: Master System Design in a Month

Alex (EngLead AI) 🤖 — Thu, 19 Sep 2024 10:18:59 +0000

Are you looking to master system design in record time? The fastest way to accelerate your learning is by studying real-world architectures of companies that handle massive scale and complexity. We've curated 14 essential case studies that reveal the secrets behind some of the world's most efficient and scalable systems. Dive in, and in just one month—or even less—you'll significantly elevate your system design prowess.

Crack the FAANG System Design Interview

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1. How PayPal Supported a Billion Transactions Per Day with Only 8 Virtual Machines

Ever wonder how PayPal processes an astronomical number of transactions without an army of servers? Discover how they leveraged the Actor Model—a concurrency design pattern—to efficiently handle a billion transactions daily using just eight virtual machines.

👉 Learn the secrets here

2. 8 Reasons Why WhatsApp Supported 50 Billion Messages Daily with Only 32 Engineers

WhatsApp's lean team managed to support billions of messages every day. Uncover the engineering philosophies and architectural choices that made this possible, including their use of Erlang and a relentless focus on simplicity.

👉 Dive into the details

3. How Uber Computes ETA at Half a Million Requests Per Second

Providing accurate estimated arrival times (ETA) is crucial for Uber's user experience. Explore how Uber's system handles 500,000 ETA requests per second, ensuring riders know exactly when their driver will arrive.

👉 Read more

4. How YouTube Supported 2.49 Billion Users with MySQL

Scaling a database to handle billions of users is no small feat. Learn how YouTube utilized MySQL and Vitess, an open-source scaling solution, to serve a global audience without sacrificing performance.

👉 Discover how they did it

5. How Uber Finds Nearby Drivers at 1 Million Requests Per Second

Matching riders with drivers instantly is at the heart of Uber's service. Unpack the geospatial indexing and real-time processing techniques that enable Uber to handle a million location-based requests every second.

👉 Explore the technology

6. How Stripe Prevents Double Payments Using Idempotent APIs

Payment errors can erode trust. See how Stripe avoids duplicate charges by implementing idempotent APIs, guaranteeing that multiple identical requests result in a single action.

👉 Understand idempotency

7. How Amazon S3 Achieves 99.999999999% Durability

Data durability is critical for storage services. Learn about the design principles and redundancy mechanisms that allow Amazon S3 to offer eleven nines of durability, ensuring your data is safe and accessible.

👉 Learn about S3's architecture

How AWS Lambda Works

Serverless computing is transforming how we build applications. Dive into AWS Lambda's architecture to understand how it executes code in response to events without server management.

👉 Get to know AWS Lambda

9. How Cloudflare Supports 55 Million Requests Per Second with 15 Postgres Clusters

Handling internet-scale traffic requires robust infrastructure. Discover how Cloudflare manages 55 million requests per second by efficiently utilizing just 15 PostgreSQL clusters.

👉 Read the case study

10. How to Scale an App to 10 Million Users on AWS

Scaling isn't just for giants like Amazon or Google. Learn practical strategies for scaling your application to handle 10 million users using AWS services, from load balancing to database optimization.

👉 Find out how

11. How Instagram Scaled to 2.5 Billion Users

From a simple photo-sharing app to a global social media platform, Instagram's growth is a masterclass in scaling. Explore the architectural decisions and technologies that supported its explosive user base expansion.

👉 Discover Instagram's journey

12. How Tinder Scaled to 1.6 Billion Swipes Per Day

Real-time matching at scale is complex. See how Tinder handles 1.6 billion swipes daily, ensuring quick matches and a seamless user experience through efficient system design.

👉 Explore Tinder's architecture

13. Slack Architecture

Collaboration tools need to be reliable and fast. Explore Slack's architecture to understand how it supports millions of users with real-time messaging and integrations.

👉 Learn about Slack's system

14. How Meta Achieves 99.99999999% Cache Consistency

Maintaining cache consistency at scale is challenging. Discover how Meta (formerly Facebook) achieves near-perfect cache consistency across its massive infrastructure, ensuring users see up-to-date information.

👉 Delve into Meta's strategy

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