Linux Matlab
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MatLab with Ubuntu 11.10

Under the new Ubuntu release (11.10 Oneiric Ocelot), trying to run MatLab gets the following error

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/opt/MATLAB/bin/util/oscheck.sh: 605: /lib64/libc.so.6: not found

MatLab will still load, but will effectively be useless and can only be closed by killing the process. Furthermore, my previous trick of

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sudo ln -s /lib64/x86_64-linux-gnu/libc-2.13.so /lib64/libc.so.6

On closer inspection, the libc.so.6 link exists in /lib64 however the file it actually points to (/lib64/x86_64-linux-gnu/libc-2.13.so) does not exist, thanks to the update it’s moved

For 64 bit:

Remove old link

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sudo rm /lib64/libc.so.6

(Be careful when removing the old link. It is essential for Linux to boot. So if you reboot before replacing the link with the new version then it’ll be time to dig out a live disk. If you’re paranoid then you can copy cp the libc.so.6 file to something like libc.so.6.backup first.)

Replace with new link

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sudo ln -s /lib/x86_64-linux-gnu/libc-2.13.so /lib64/libc.so.6

For 32 bit:

(I have not tested this as I only run x64 systems, but from the comments I think it should work)

Remove old link (again be careful with this command)

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sudo rm /lib32/libc.so.6

Replace with new link

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sudo ln -s /lib/i386-linux-gnu/libc-2.13.so /lib/libc.so.6

Of course if this is a fresh Ubuntu install then you probably wont need the rm (remove) command.

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Linux Pymol
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PyMOL visualising e2 rotamers

This morning whilst writing some reports I wanted to show some rotamer populations of my spin label attached to the protein. Whilst MMM provides visualisation, and having had discussions with Jeschke I understand just how difficult it is to do, I do …

Technology Linux Backup
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Logarithmic backups on a linux server

This is the second part of the lab server setup guide. Mainly for my own memory purposes, but if you find it useful then so much the better.

Synchronization

As previously discussed all of the machines in the lab are mounted onto the server using NFS …

Technology Linux Server
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Lab server setup - Ubuntu 12.4

With a recent change of machines in the lab, I thought that I should update the backup server according to reflect the new machines. However, upon inspection the backup server OS hard drive had died long ago; but then what do you expect when you use …

LEADERSHIP, SCALE, AND THE HUMAN ELEMENT

Principles & Perspectives

How do you define successful engineering leadership?

The Philosophy

Many view technical leadership as being the “smartest architect in the room.” I see it as the opposite. My job is to build a room where I don’t have to be the smartest person because the systems, culture, and communication are so robust that the team can out-innovate me.

The Strategy

  • Alignment: Does every engineer understand how their sprint task impacts the company’s bottom line?
  • Velocity vs. Stability: We aren’t just “shipping fast”; we are building a predictable, repeatable engine that doesn’t collapse under its own weight at the next order of magnitude.
  • The Human Growth Curve: Success is when the engineering team’s capability evolves faster than the product’s complexity. If the team feels stagnant, the tech stack will soon follow.

What is your approach to scaling technical organizations?

The Philosophy

Scaling isn’t just “hiring more people” - that’s often how you slow down. Scaling is about moving from Individual Heroics to Organizational Systems.

The Strategy

  • The 3-Continent Perspective: Having managed global teams, I focus on “High-Signal Communication.” As you grow, the cost of a meeting triples. I implement “Asynchronous-First” cultures that protect deep-work time while ensuring no one is blocked by a timezone.

  • Modular Autonomy: I advocate for breaking down monolithic teams into autonomous units with clear ownership. This reduces the “communication tax” and allows us to scale the headcount without scaling the bureaucracy.

  • Automation as Infrastructure: At petabyte scale, manual intervention is a failure. I treat the developer experience (CI/CD, observability, self-service infra) as a first-class product to keep the “path to production” frictionless.

How do you balance high-growth velocity with technical stability?

The Philosophy

Technical debt isn’t a “bad thing” to be avoided; it’s a set of historical decisions that no longer serve you. Like any loan, leverage can accelerate growth when investments payoff. But if velocity and returns are slowing you need a payment plan before the interest kills you.

The Strategy

  • The ROI Filter: I don’t refactor for the sake of “clean code.” I don’t refactor a micro-service with no users. I refactor when the pain on that debt - measured in bugs, downtime, or developer frustration - starts to exceed the cost of the fix.

  • Zero-Downtime Culture: Especially at scale, stability is a feature. I implement “Guardrail Engineering” where the system is designed to fail gracefully, ensuring that a Series B growth spike becomes a success story rather than a post-mortem.

  • The 70/20/10 Rule: I typically aim to dedicate 70% of resources to new features, 20% to infrastructure/debt, and 10% to R&D. This ensures we never stop innovating, but we never stop fortifying either.