This article is a nice exploration of how this famed culture is evolving in a post-Besos world, and is a great reminder that culture is not the result of processes, tools or documents, but the result of leadership, communication and clear structure & policies.
Tag: technology
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In the realm of technology and software development, the phrase “shift left” has gained significant traction. At its core, the “shift left” approach emphasizes the importance of addressing issues and tasks earlier in the development process. This proactive strategy can lead to more efficient, effective, and successful technology implementation projects. Let’s delve deeper into this concept and understand its parallels with the heuristic breadth-first search.
“Shift Left”?
“Shift left” is a philosophy that encourages developers and teams to tackle potential challenges and issues at the earliest stages of a project. Instead of waiting for problems to arise during the testing or deployment phases, the idea is to anticipate and address them during the planning and development stages. This can lead to fewer surprises, reduced costs, and a smoother implementation process.
Analysing the Existing Codebase and Desired Outcomes
Before diving into a new project or adding features to an existing one, it’s crucial to analyze the current codebase. This involves understanding its strengths, weaknesses, and potential areas of improvement. By doing so, teams can identify existing technical debt and areas that might become bottlenecks or pain points in the future.
Once the current landscape is clear, the next step is to define the desired outcomes. What is the end goal? What business value is the project aiming to deliver? By having a clear vision, teams can align their efforts and ensure that every step taken is in the right direction.
Defining Slices of Work
After understanding the starting point and the destination, the journey can be broken down into manageable slices of work. Each slice should:
Deliver Business Value: Every slice should have a tangible benefit, whether it’s a new feature, an optimization, or a bug fix.
Incrementally Increase Solution Maturity: As each slice is completed, the overall solution should evolve and mature, getting closer to the desired outcome.
Reduce Technical Debt: With each slice, any existing technical debt should be addressed, ensuring that the codebase remains clean, efficient, and maintainable.
The Heuristic Breadth First Search Analogy
The process described above can be likened to a heuristic breadth-first search (BFS). In BFS, we explore all the neighbouring nodes at the present depth before moving on to nodes at the next level of depth. Similarly, in the “shift left” approach, instead of diving deep into one aspect of the project, teams tackle a broad range of tasks that deliver immediate value. This ensures that the most pressing issues and valuable features are addressed first, providing quick wins and immediate benefits to the business.
The heuristic aspect comes into play when we prioritize these slices of work based on their potential impact and value. Just as heuristics guide search algorithms to find the most promising paths, our understanding of the business needs and technical challenges guides us in choosing which slices to tackle first.
Conclusion
The “shift left” approach, when combined with the principles of analysing the existing landscape, defining clear outcomes, and breaking the journey into valuable slices, can transform the way projects are executed. By drawing inspiration from heuristic breadth-first search, teams can ensure that they are always moving in the right direction, delivering value at every step, and building solutions that increase the overall maturity of the codebase.
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Contact tracing, the process by which health authorities can identify those at risk of infection by virtue of proximity to a known case of a virus, …
Link to full post on Fjellfolk: On privacy and contact tracing -
Contact tracing, the process by which health authorities can identify those at risk of infection by virtue of proximity to a known case of a virus, has long been a pillar of pandemic response. With radio-enabled mobile devices now globally ubiquitous, it comes as little surprise to see that they are playing a role in the evolution of contact tracing methods.
Contact tracing is only as effective as its scale. The ability to evaluate only small proportions of an at-risk population will ultimately undermine any contact tracing system, manual or digital. As early attempts to develop contact tracing apps launched around the globe, technical limitations, privacy concerns and poor assumptions about adoption rates undermined almost all efforts. Into this mess stepped Apple and Google, together responsible for the software, and to a lesser extent hardware, which power the majority of our mobile devices.
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The next generation of mobile broadband data network is being discussed in almost every context you can imagine, from technology to healthcare to sociology to urbanism. 5G is coming. But what is it, what does it enable, and how is it relevant to the citizens of urban places? And therefore, what does it mean to those designing and moulding the cities in which we live?
5G will replace or augment your existing 4G connection, providing exponentially greater bandwidth alongside massively reduced latency – the time it takes for data to get from A to B.
5G operates across a broad range of frequency spectrums. Lower frequency ranges (below 6GHz) provide a more reliable signal but are limited in their bandwidth. These ranges are nearing saturation in many cases from overloading of existing 4G networks. 5G also leverages higher frequency spectrums, which provide massively increased data rates and much lower latency, but which are quite limited in their ability to penetrate buildings, and the coverage area for a single antenna is limited, thus necessitating much larger numbers of antennae to achieve uniform and reliable coverage.
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In this paper Geoff Boeing examines ways in which the Smart Cities paradigm can leverage publicly sourced datasets to enhance traditional forms of …
Link to full post on Fjellfolk: Big Data in Urban Morphology -
In this paper Geoff Boeing examines ways in which the Smart Cities paradigm can leverage publicly sourced datasets to enhance traditional forms of urban form analysis and visualisation to further our understanding of urban morphology.
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Many people today are talking about how uncomfortable they feel about their dependence on devices, and more specifically the content they consume via their devices. Whilst this theme is certainly well established, I have observed very few experimenting with ways of adapting to this trend in a positive manner.
What is interesting is how, in a market where devices are sold on the basis of how they can be depended upon to provide a solution to our needs (and they do), it is often an entirely different kind of dependency which emerges as the dominant factor in our relationship with our devices. Specifically we are using our devices to medicate boredom and anxiety, and later choosing feeds over friends once the dependency has been established. This is not a healthy dependency, in many circumstances probably as undesirable as the long term use of alcohol or recreational drugs for the treatment of these states of mind. Like drugs and alcohol it probably doesn’t feel like a problem until it is too late. But unlike drugs or alcohol, the consequences are less direct, more emergent.
Our devices have quietly inserted themselves into our lines of communication with friends and social circles, into the time and space which we spend with our friends, and thus into the fabric of our relationships. And they are so effective at entertaining, and at incentivising further consumption, that unassuming victims begin to choose, consciously or unconsciously, their devices over their relationships. Think of the husband on the sofa, scrolling through twitter rather than acknowledging his wife’s needs, or the teenage girl, one of many looking at each other only through the lens (literal or otherwise) of what might make a popular post on snapchat or instagram.
As our minds adapt to the growing strength of each hit, so the systems evolve new means to keep us engaged, and in a zero-sum game, our attention on people and relationships slips away, slowly at first, but the snowball is gaining momentum.
For me, the time has come to acknowledge this phenomenon and act. My children deserve my undivided attention, my wife deserves the best of me, and my relationships with wider family and social circles would benefit far more from a phone call than from a rushed WhatsApp message. And so I’m running an experiment. I will switch to a feature phone for a month or so. I’ve selected a device with enough features to ensure the impact on my ability to do my job is not impacted, but with enough compromises in user experience to be an undesirable channel through which to consume, regardless of its theoretical capabilities.
This experiment requires concessions, but I had in mind some capabilities which would help me strike the right balance between convenience and addictive consumption. On my list of considerations were:
- No qwerty keyboard – if I really need to type, I can take the time to pull out an ipad or laptop
- Small screen – if the phone happens to have a twitter app, let’s make sure it’s a crappy experience
- 4G with hotspot capabilities so I can continue to work remotely from my ipad
- Basic maps so I can navigate if I’m in a tough spot
- MP3 player and bluetooth – I can live without Twitter, but not without Bill Evans…
Some concerns I have going in to this experiment are around the ways in which a mobile phone has become so convenient for tasks which aren’t about consumption. For example, I use my phone to manage task lists, to write stubs of blog posts, to take and edit photos, to manage and respond to emails, to pay using Apple Pay, to manage bank accounts and make payments, to hire electric scooters, to study on Coursera or Memrise, to plan routes on public transport, to hire cars… The list of what I am likely to miss is endless, and that is part of the problem.
My hope is that, with a handy partner device (the ipad on which I am writing this post), I will be able to enjoy enough of the convenience of this long list of capabilities without the excessive convenience which so easily leads to a slip in to mindless consumption. Because it leaves me numb, burns my time and feeds my anxiety, and that ain’t good.
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A stark reminder this week that there are two sides to the renewable energy revolution: supply and demand. Whilst we in Denmark can be proud of our record on the supply side of this equation, the recent increase in interest from big tech in locations with good renewable credentials does suggest that governments may be called upon to impose some control on the demand side also.
The problem is that Denmark’s green energy has its limits. There is no way that existing renewables facilities could hope to meet the prospective demand from data centers.
https://www.citylab.com/environment/2018/06/denmarks-carbon-footprint-is-set-to-rise-sharply/563486/ -
Evolving Specialised Species in Diverse Simulated Ecologies using a Subsumption ArchitectureDownload
Back in 2006, three years in to my degree in “computer systems and software engineering” at the University of York, I embarked on my Masters project. The ambition was quite clear – to use a complementary suite of AI algorithms to simulate speciation. The choice of algorithm for behaviour selection, Rodney Brooks’ subsumption architecture, was dictated by my supervisor, but despite the existence of prior work on the use of the subsumption architecture, the existing implementation was missing key prerequisites for achieving the ambition of the project. Specifically the ability to vary the geographic (and other environmental) conditions such that speciation might occur.
Whilst I learned many things from the arduous process of trying to run successful experiments on this journey, none were so precious as the respect I developed for the complexity of systems. The combination of two core AI algorithms, a broad range of environmental variables and the entropy of unpredictable individual behaviour across a population of hundreds over thousands of generations, led to a huge degree of complex, emergent outcomes. Controlling these outcomes became a huge challenge for me, preventing me from obtaining the evidence I needed to support some conclusions I knew it was possible to draw. The experience was humbling, educational and frustrating in equal parts. The write up I submitted can be viewed at this link. I will eventually edit and post a subsequent revision which includes conclusions drawn after further experimentation past the submission deadline!
