It really sucks to be a software engineer right now.
Heck, it sucks to be in almost any role at a tech company right now.
The layoffs are exhausting. The job market feels strange. AI is changing the work faster than most people have had time to process. And a lot of people are wondering what happens next.
I made a video about why this moment feels so hard, what's changing, and how to prepare for what's coming.
Please watch here:
https://t.co/qDvJ0kpR3V
Would love to hear how you're thinking about all of this.
It really sucks to be a software engineer right now.
Heck, it sucks to be in almost any role at a tech company right now.
The layoffs are exhausting. The job market feels strange. AI is changing the work faster than most people have had time to process. And a lot of people are wondering what happens next.
I made a video about why this moment feels so hard, what's changing, and how to prepare for what's coming.
Please watch here:
https://t.co/qDvJ0kpR3V
Would love to hear how you're thinking about all of this.
Can the company behind an AI tool read what I type into it?
Maybe. It depends on the product, plan, settings, and contract.
If the tool runs in the cloud, your prompt leaves your machine. After that, the details matter: retention, logging, training use, abuse monitoring, support access, enterprise controls, and how uploaded files are handled.
Sensitive data isn't just passwords.
It can be contracts, customer logs, support tickets, source code, incident notes, unreleased plans, security findings, database exports, private messages, or internal prompts.
The chat box feels casual. The data boundary isn't.
Before pasting production-adjacent material, know which account you're using and what policy applies. If you don't know, redact or use synthetic examples.
IBM Research released MAMMAL, a unified 458-million parameter foundation model that processes genes, proteins, and molecules in a single shared framework.
https://t.co/w4Wk0lNFAb
What does it mean when an AI app says it has "memory"?
Usually it means the app stores notes and can reuse them later.
It doesn't mean the model has a perfect diary of your chats.
The product might save your name, stack, writing style, project, preferred format, or recurring instructions. Later, it may insert some of those notes into the context before the model answers.
That can help. It can also go stale.
Maybe it remembers an old project. Maybe it applies a tone preference where it doesn't belong. Maybe it keeps a rule after the rule changed.
Memory is stored context, not human continuity.
The healthiest version is inspectable. If a tool remembers things about you, you should be able to see, edit, and delete what it saved.
Why is AI good at explaining code but still capable of breaking a project?
Because local code is easier than system behavior.
A single function gives the model strong clues: names, imports, types, comments, control flow, and error messages. It can often explain that piece well.
A project has contracts the function doesn't show.
A caller may depend on old behavior. A route may run in a different runtime. A migration may hit old data. A generated file may overwrite the edit. A test fixture may hide the real production shape.
So the model can be right about the snippet and wrong about the change.
This isn't unique to AI. Humans break projects the same way when they only inspect the local file.
Explaining code means understanding what it says. Changing a project means understanding what depends on it.
Learn how to deploy Claude Code in multi-million line monorepos using hierarchical context, language server protocol integration, and on-demand skills.
https://t.co/EatZXz3DHO
IBM released Granite Embedding Multilingual R2, upgrading its Apache 2.0 encoder models with a 32,768-token context window and ModernBERT architecture.
https://t.co/QKFNZJnNke
Anthropic has restricted its new Claude Mythos model to select partners after pre-release testing revealed autonomous cyberattack capabilities.
https://t.co/v9xMWnfuQQ
Google and Arm have integrated SME2 micro-kernels into LiteRT, accelerating on-device generative AI workloads by up to 5x without custom assembly code.
https://t.co/KEPp1vrvyG
Learn how to use Google's new Genkit Middleware to intercept model calls, implement human-in-the-loop tool approvals, and handle transient API failures.
https://t.co/Ia6vj8CYbJ