The most notable improvement comes from CloningQA, which requires end-to-end design of DNA and enzyme reagents for molecular cloning protocols.

Claude Opus 4.7 autonomously built a complete Rust text-to-speech engine from scratch—neural model, SIMD kernels, browser demo—then fed its own output through a speech recognizer to verify it matched the Python reference.

Residual ISD (R-ISD) adds a gated LoRA adapter for bit-for-bit lossless acceleration: LoRA active only at MASK positions; verify positions use base-only weights Output is identical to the base AR model by construction

AI writes the code. Tests verify correctness. More code enables more features.

On the SWE-Pro benchmark, M2.7 scores 56.22%, nearly matching Opus's best level.

M2.7 demonstrates excellent performance in real-world software engineering, including end-to-end project delivery, log analysis for bug hunting, code security, and machine learning tasks.

On the SWE-Pro benchmark, M2.7 scores 56.22%, nearly matching Opus's best level.

Claude Opus 4.6 autonomously reimplemented a 16,000-line bioinformatics toolkit — a task we believe would take a human engineer weeks.

The prompt is the most important part: the routine runs autonomously, so the prompt must be self-contained and explicit about what to do and what success looks like.

Same clinical question, two framings. One as a patient, one as a doctor.

Built-in memory works out of the box

GLM-5.1 achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).

When you give a task to your agent, make sure you also explain how the code should be organized. Not only value, but also structure.

Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase.

inappropriately change or overwrite JSON files compared to Markdown files

improved with grading criteria that encode design principles and preferences.

State is explicit. CWD, env vars, and config paths are inputs, not assumptions

There's an old saying that content is king. With agents, context is.

Contextual Drag: How Errors in the Context Affect LLM Reasoning

Our key finding is that these representations causally influence the LLM's outputs, including Claude's preferences and its rate of exhibiting misaligned behaviors such as reward hacking, blackmail, and sycophancy.

Without any architectural modification, MinerU2.5-Pro achieves 95.69 on OmniDocBench v1.6, improving over the same-architecture baseline by 2.71 points and surpassing all existing methods including models with over 200× more parameters.

Looking under the hood is cheating. You're only supposed to have vague conversations with the machine about what it's doing.

Teams at companies like Notion, Ramp, Braintrust, and Wasmer are already using Codex to accelerate their engineering workflows.

Owning a $5M data center

must extend beyond that which appears solely technical into the realm of social relations.

the hidden labour of engineering

There is a tremendous power in thinking about everything as a single kind of thing, because then you don’t have to juggle lots of different ideas about different kinds of things; you can just think about your problem.

what would it look like to put together an engineering program or an experimental program um orienting towards the question of what would be the form of embodiment in collective intelligence that includes human beings as at least one primary element at ontological level one that would give rise to a collective intelligence at ontological level two

That West German engineering mixed with decades of tar and nicotine has produced something truly unique.

The challenge with previous generations of tech—and the engineers who built them—is that they got stuck in the rigidity of systems.

you may never change it back just as we 00:34:52 may not change back genetic manipulation we might do in in the germline which is a reason we should be very cautious about doing it

We have always done geo-engineering, from the prairies of Native Americans to European forests, from Indian to Chinese rice fields. Now that we can no longer deny our impact and the responsibility it entails, it’s time to open our eyes and consciously do geo-engineering.

Getting hooked on computers is easy—almost anybody can make a program work, just as almost anybody can nail two pieces of wood together in a few tries. The trouble is that the market for two pieces of wood nailed together—inexpertly—is fairly small outside of the "proud grandfather" segment, and getting from there to a decent set of chairs or fitted cupboards takes talent, practice, and education.

the Peter Principle, the idea that in an organization where promotion is based on achievement, success, and merit, that organization's members will eventually be promoted beyond their level of ability

This is not a discrete project but an ongoing process and should always be competing for focus in strategic decision making.

whoever came up with Apple's property list format didn't really understand how XML and/or SGML-like tags actually worked. Does it make sense to you that p-lists have stuff like <key>WebResourceData</key> instead of simply just <WebResourceData> ? It's like they were confused

By jumping into unfamiliar areas of code, even if you do not "solve" the bug, you can learn new areas of the code, tricks for getting up to speed quickly, and debugging techniques.

Thinking about how you will observe whether things are working correctly or not ahead of time can also have a big impact on the quality of the code you write.

“Let me know if you have any more questions,”

The most important takeaway from exposing these myths is that productivity cannot be reduced to a single dimension (or metric!). The prevalence of these myths and the need to bust them motivated our work to develop a practical multidimensional framework, because only by examining a constellation of metrics in tension can we understand and influence developer productivity. This framework, called SPACE, captures the most important dimensions of developer productivity: satisfaction and well-being; performance; activity; communication and collaboration; and efficiency and flow

The thing is I'm finding trouble to dedicate to Ransack lately, so I can't really commit to any date.

engineering a safer world it's done by nancy levison who is a aerospace astronautics engineer

1: Why Do We Need Something Different? Doi: https://doi.org/10.7551/mitpress/8179.003.0004 Open the PDF Link PDF for 1: Why Do We Need Something Different? in another window 2: Questioning the Foundations of Traditional Safety Engineering Doi: https://doi.org/10.7551/mitpress/8179.003.0005 Open the PDF Link PDF for 2: Questioning the Foundations of Traditional Safety Engineering in another window 3: Systems Theory and Its Relationship to Safety Doi: https://doi.org/10.7551/mitpress/8179.003.0006 Open the PDF Link PDF for 3: Systems Theory and Its Relationship to Safety in another window II: STAMP: An Accident Model Based On Systems Theory [ Opening ] Doi: https://doi.org/10.7551/mitpress/8179.003.0029 Open the PDF Link PDF for [ Opening ] in another window 4: A Systems-Theoretic View of Causality Doi: https://doi.org/10.7551/mitpress/8179.003.0008 Open the PDF Link PDF for 4: A Systems-Theoretic View of Causality in another window 5: A Friendly Fire Accident Doi: https://doi.org/10.7551/mitpress/8179.003.0009 Open the PDF Link PDF for 5: A Friendly Fire Accident in another window III: Using STAMP [ Opening ] Doi: https://doi.org/10.7551/mitpress/8179.003.0030 Open the PDF Link PDF for [ Opening ] in another window 6: Engineering and Operating Safer Systems Using STAMP Doi: https://doi.org/10.7551/mitpress/8179.003.0011 Open the PDF Link PDF for 6: Engineering and Operating Safer Systems Using STAMP in another window 7: Fundamentals Doi: https://doi.org/10.7551/mitpress/8179.003.0012 Open the PDF Link PDF for 7: Fundamentals in another window 8: STPA: A New Hazard Analysis Technique Doi: https://doi.org/10.7551/mitpress/8179.003.0013 Open the PDF Link PDF for 8: STPA: A New Hazard Analysis Technique in another window 9: Safety-Guided Design Doi: https://doi.org/10.7551/mitpress/8179.003.0014 Open the PDF Link PDF for 9: Safety-Guided Design in another window 10: Integrating Safety into System Engineering Doi: https://doi.org/10.7551/mitpress/8179.003.0015 Open the PDF Link PDF for 10: Integrating Safety into System Engineering in another window 11: Analyzing Accidents and Incidents (CAST) Doi: https://doi.org/10.7551/mitpress/8179.003.0016 Open the PDF Link PDF for 11: Analyzing Accidents and Incidents (CAST) in another window 12: Controlling Safety during Operations Doi: https://doi.org/10.7551/mitpress/8179.003.0017 Open the PDF Link PDF for 12: Controlling Safety during Operations in another window 13: Managing Safety and the Safety Culture Doi: https://doi.org/10.7551/mitpress/8179.003.0018 Open the PDF Link PDF for 13: Managing Safety and the Safety Culture in another window 14: SUBSAFE: An Example of a Successful Safety Program Doi: https://doi.org/10.7551/mitpress/8179.003.0019 Open the PDF Link PDF for 14: SUBSAFE: An Example of a Successful Safety Program in another window Epilogue Doi: https://doi.org/10.7551/mitpress/8179.003.0020 Open the PDF Link PDF for Epilogue in another window Appendixes A: Definitions Doi: https://doi.org/10.7551/mitpress/8179.003.0022 Open the PDF Link PDF for A: Definitions in another window B: The Loss of a Satellite Doi: https://doi.org/10.7551/mitpress/8179.003.0023 Open the PDF Link PDF for B: The Loss of a Satellite in another window C: A Bacterial Contamination of a Public Water Supply Doi: https://doi.org/10.7551/mitpress/8179.003.0024 Open the PDF Link PDF for C: A Bacterial Contamination of a Public Water Supply in another window D: A Brief Introduction to System Dynamics Modeling Doi: https://doi.org/10.7551/mitpress/8179.003.0025 Open the PDF Link PDF for D: A Brief Introduction to System Dynamics Modeling in another window References Doi: https://doi.org/10.7551/mitpress/8179.003.0026 Open the PDF Link PDF for References in another window Index Doi: https://doi.org/10.7551/mitpress/8179.003.0027 Open the PDF Link PDF

synthetic bioengineering provides a really astronomically large option space for new bodies and new minds that don't have 00:04:28 standard evolutionary backstories

For example, productivity and satisfaction are correlated, and it is possible that satisfaction could serve as a leading indicator for productivity; a decline in satisfaction and engagement could signal upcoming burnout and reduced productivity.

• Design and coding. Volume or count of design documents and specs, work items, pull requests, commits, and code reviews. • Continuous integration and deployment. Count of build, test, deployment/release, and infrastructure utilization. • Operational activity. Count or volume of incidents/issues and distribution based on their severities, on-call participation, and incident mitigation.

The SPACE framework provides a way to logically and systematically think about productivity in a much bigger space and to carefully choose balanced metrics linked to goals—and how they may be limited if used alone or in the wrong context.

engineering blogs focus on problems where the solution is a necessary but not sufficient part of what they do. And, ideally, they focus on problems that are complementary to scale that only the publisher of that post has.

Platform engineering is trying to deliver the self-service tools teams want to consume to rapidly deploy all components of software. While it may sound like a TypeScript developer would feel more empowered by writing their infrastructure in TypeScript, the reality is that it’s a significant undertaking to learn to use these tools properly when all one wants to do is create or modify a few resources for their project. This is also a common source of technical debt and fragility. Most users will probably learn the minimal amount they need to in order to make progress in their project, and oftentimes this may not be the best solution for the longevity of a codebase. These tools are straddling an awkward line that is optimized for no-one. Traditional DevOps are not software engineers and software engineers are not DevOps. By making infrastructure a software engineering problem, it puts all parties in an unfamiliar position. I am not saying no-one is capable of using these tools well. The DevOps and software engineers I’ve worked with are more than capable. This is a matter of attention. If you look at what a DevOps engineer has to deal with day-in and day-out, the nuances of TypeScript or Go will take a backseat. And conversely, the nuances of, for example, a VPC will take a backseat to a software engineer delivering a new feature. The gap that the AWS CDK and Pulumi try to bridge is not optimized for anyone and this is how we get bugs, and more dangerously, security holes.

But the researchers quickly realized that a model’s complexity wasn’t the only driving factor. Some unexpected abilities could be coaxed out of smaller models with fewer parameters — or trained on smaller data sets — if the data was of sufficiently high quality. In addition, how a query was worded influenced the accuracy of the model’s response.

prompt engineer. His role involves creating and refining the text prompts people type into the AI in hopes of coaxing from it the optimal result. Unlike traditional coders, prompt engineers program in prose, sending commands written in plain text to the AI systems, which then do the actual work.

In my most recent field (software), the engineer is placated with the delusion that the purpose is to give the customer what ey wants, whether that solves the customer’s problem or not. This is a lazy sort of self-imposed servitude that entirely avoids the actual purpose of engineering.

Including a prompt prefix in the chain-of-thought style encourages the model to generatefollow-on sequences in the same style, which isto say comprising a series of explicit reasoningsteps that lead to the final answer. This abilityto learn a general pattern from a few examples ina prompt prefix, and to complete sequences in away that conforms to that pattern, is sometimescalled in-context learning or few-shot prompt-ing. Chain-of-thought prompting showcases thisemergent property of large language model at itsmost striking.

Dialogue is just one application of LLMs thatcan be facilitated by the judicious use of promptprefixes. In a similar way, LLMs can be adaptedto perform numerous tasks without further train-ing (Brown et al., 2020). This has led to a wholenew category of AI research, namely prompt en-gineering, which will remain relevant until wehave better models of the relationship betweenwhat we say and what we want.

In the background, the LLM is invisiblyprompted with a prefix along the following lines.

In the near future, we will be in possession of genetic engineering technology which allows us to move genes precisely and massively from one species to another. Careless or commercially driven use of this technology could make the concept of species meaningless, mixing up populations and mating systems so that much of the individuality of species would be lost. Cultural evolution gave us the power to do this. To preserve our wildlife as nature evolved it, the machinery of biological evolution must be protected from the homogenizing effects of cultural evolution.

In the near future, we will be in possession of genetic engineering technology which allows us to move genes precisely and massively from one species to another. Careless or commercially driven use of this technology could make the concept of species meaningless, mixing up populations and mating systems so that much of the individuality of species would be lost. Cultural evolution gave us the power to do this. To preserve our wildlife as nature evolved it, the machinery of biological evolution must be protected from the homogenizing effects of cultural evolution.

Deploy engines as separate app instances and have them only communicate over network boundaries. This is something we’re starting to do more.

I always allocate a year: six months to get up to speed on the internal culture, tools, and processes; another six months to get your first performance review as a “ramped-up” engineer.

While you might think that pairing less experienced engineers is a waste of time, every single time I had a less experienced engineer work by themselves, I ended up regretting it.

engineers will get tired, mistakes will happen, and maintenance will get kicked down the road. Teams need buffer as much as systems do.

L’ingénierie est un processus structuré et méthodique qui permet de passer de besoins opérationnels à la mise en œuvre d’une solution.

Cela correspondrait à ne pas faire le travail d’ingénierie, car il n’y aurait pas eu d’analyse.

One example could be putting all files into an Amazon S3 bucket. It’s versatile, cheap and integrates with many technologies. If you are using Redshift for your data warehouse, it has great integration with that too.

It took me a while to grok where dbt comes in the stack but now that I (think) I have it, it makes a lot of sense. I can also see why, with my background, I had trouble doing so. Just as Apache Kafka isn’t easily explained as simply another database, another message queue, etc, dbt isn’t just another Informatica, another Oracle Data Integrator. It’s not about ETL or ELT - it’s about T alone. With that understood, things slot into place. This isn’t just my take on it either - dbt themselves call it out on their blog:

Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.

it was clear that the European and US competitors werebenefiting from these changes to the curriculum in advances in commerce, inindustry, and even on the battlefield.

There has been much discussion about “atomic notes”, which represents the key ideas from a person’s research on some topic or source (sources one and two). These are not the kind of thing I am interested in creating/collecting, or at least not what I have been doing. A far more typical thing for me is something I did at work today. I was trying to figure out how to convert the output of a program into another format. I did some searching, installed a tool, found a script, played with the script in the tool, figured out how to use it, then wrote down a summary of my steps and added links to what I found in my search. Since I am not doing research for a book or for writing academic papers, the idea of an atomic note does not fit into my information world. However, capturing the steps of a discovery or how I worked out a problem. is very real and concrete to me. I used to know a fellow engineer who wrote “technical notes” to capture work he was doing (like a journal entry). Maybe that is how I should consider this type of knowledge creation. 

The major benefit of foreign keys is that they guarantee referential integrity. For example, say you have customers in one table that may refer to a number of invoices in another. Without foreign keys, you could delete a customer, but forget to remove its invoices, thereby leaving a bunch of orphaned invoices that reference an customer that’s gone.

If an operator ever queries the database directly they’re even more likely to forget deleted_at because normally the ORM does the work for them.

At the same time, like Harold, I’ve realised that it is important to do things, to keep blogging and writing in this space. Not because of its sheer brilliance, but because most of it will be crap, and brilliance will only occur once in a while. You need to produce lots of stuff to increase the likelihood of hitting on something worthwile. Of course that very much feeds the imposter cycle, but it’s the only way. Getting back into a more intensive blogging habit 18 months ago, has helped me explore more and better. Because most of what I blog here isn’t very meaningful, but needs to be gotten out of the way, or helps build towards, scaffolding towards something with more meaning.

Outline of academic disciplines

Robert Fenton, Electrical and Computer Engineering Professor Emeritus, pioneered the technology for the first wave of self-driving cars.

systems

Much of this sort of information was later reverse-engineered, and cross-browser support for basic operations is actually quite good. (Browsers still vary widely on the details.)

Reverse-engineering and standardizing contentEditable

three steps required to solve the all-importantcorrespondence problem. Step one, according to Shenkar: specify one’s ownproblem and identify an analogous problem that has been solved successfully.Step two: rigorously analyze why the solution is successful. Jobs and hisengineers at Apple’s headquarters in Cupertino, California, immediately got towork deconstructing the marvels they’d seen at the Xerox facility. Soon theywere on to the third and most challenging step: identify how one’s owncircumstances differ, then figure out how to adapt the original solution to thenew setting.

innovation can result in trade-offs that undermine both progress on mitigation and 12 progress towards other sustainable development goals

It was not until the 14th century that scientists created the first sophisticated astronomical clocks.

the first precision-geared mechanism known is a relatively simple—yet impressive for the time—geared sundial and calendar of Byzantine origin dating to about C.E. 600.

In comparison, the average household energy use for gas heating in Belgium – which has a moderate climate – is 20,000 kWh per year. Assuming that the average Belgian heating system is used for six months per year, daily energy use corresponds to 109.6 kWh per day. This energy could heat roughly 900 water bottles per day – enough to keep the whole neighbourhood comfortable.

Graham Allison and Niall Ferguson have called for an “applied history” movement, to better draw lessons from history and apply them to real-world problems, including through the advising of political leaders.

When a product manager trusts that the engineers on the team have the interest of the product at heart, they also trust the engineer’s judgment when adding technical tasks to the backlog and prioritizing them. This enables the balanced mix of feature and technical work that we’re aiming for.

Applying Systems Engineering to Policy

I didn't have to think too much of what’s good for the company - I usually assumed that whatever I’m doing is what the business needs.