Feed aggregator

Article URL: https://nehalslearnings.substack.com/p/i-should-have-loved-biology-too

Comments URL: https://news.ycombinator.com/item?id=43764076

Points: 8

# Comments: 0

Article URL: https://www.tomshardware.com/pc-components/hdds/wd-launches-hdd-recycling-process-that-reclaims-rare-earth-elements-cuts-out-china

Comments URL: https://news.ycombinator.com/item?id=43764075

Points: 2

# Comments: 0

Article URL: https://openjdk.org/jeps/513

Comments URL: https://news.ycombinator.com/item?id=43764063

Points: 1

# Comments: 0

Article URL: https://privacysandbox.com/news/privacy-sandbox-next-steps/

Comments URL: https://news.ycombinator.com/item?id=43764062

Points: 3

# Comments: 0

I run a small AI company in Luxembourg. We started out as a consulting studio, building custom tools for clients — mostly boring things like dashboards, reporting modules, and CRUD backends.

At some point I realized we were building the same things over and over again. Not in a copy-paste way, but in a “we could generate 80% of this” kind of way. So last year, I ran a live-fire experiment: I asked Claude 3.5 and DeepSeek to build a small admin panel, with tests and API docs, from a plain-language spec.

The result: not great, but usable. It gave us the idea to stop typing code altogether.

Now, at Easylab AI, we don’t write code manually anymore. We use a stack of LLM-powered agents (Claude, DeepSeek, GPT-4) with structured task roles: • an orchestrator agent breaks down the spec • one agent builds back-end logic • another generates test coverage • another checks for security risks • another synthesizes OpenAPI docs • and humans only intervene for review & deployment

Agents talk via a shared context layer we built, and we introduced our own protocol (we call it MCP — Model Context Protocol) to define context flow and fallback behavior.

It’s not perfect. Agents hallucinate. Chaining multiple models can fail in weird ways. Debugging LLM logic isn’t always fun. But…

We’re faster. We ship more. Our team spends more time on logic and less on syntax. And the devs? They’re still here — but they’ve become prompt architects, QA strategists, and AI trainers.

We built Linkeme.ai entirely this way — an AI SaaS for generating social media content for SMEs. It would’ve taken us 3 months before. It took 3 weeks.

Happy to share more details if anyone’s curious. AMA.

Comments URL: https://news.ycombinator.com/item?id=43764039

Points: 1

# Comments: 1

Article URL: https://www.csoonline.com/article/3964668/hackers-target-apple-users-in-an-extremely-sophisticated-attack.html

Comments URL: https://news.ycombinator.com/item?id=43764032

Points: 2

# Comments: 0

Article URL: https://www.aacu.org/newsroom/a-call-for-constructive-engagement

Comments URL: https://news.ycombinator.com/item?id=43764022

Points: 1

# Comments: 0

Article URL: https://www.csoonline.com/article/3961304/ai-hallucinations-lead-to-new-cyber-threat-slopsquatting.html

Comments URL: https://news.ycombinator.com/item?id=43764010

Points: 3

# Comments: 1

Stop right there, criminal scum!

Hansi Flick's Blaugrana look to keep their treble challenge on track as they host Los Bermellones.

Here are some highly rated flicks to try, plus a look at what's new in April.

Cyber ‘agony aunts’ Amelia Hewitt and Rebecca Taylor are launching a book aimed at empowering women in their cyber security careers

IT security is now a metric in the Microsoft employee appraisal process

It's a potentially pivotal match in the race for Champions League qualification at the Etihad today.

The highs of making impossible plays outweigh dying to overpowered builds, but I worry about stompiness and counters.

From speedy fiber internet to budget-friendly broadband, our experts have found the best internet plans and providers for your home.

How good is your recycling knowledge? Participate in Earth Day and make our world greener by identifying which items are actually recyclable.

Over the last year, we’ve been building CodeAnt AI, working closely with engineering teams struggling with code review quality and speed.

Manual code reviews are slow and repetitive. Reviews today mostly look at what changed — not what the change actually impacts. With more AI-written code, it's getting worse: bigger PRs, faster cycles, less team context.

We wanted to rethink how code reviews are done: → Build structured knowledge of the codebase → Understand infra and dependency changes → Analyze blast radius automatically at PR time

What CodeAnt AI Does (Technical Overview)

Repository Indexing and Graph Building:

When a repo is added, we index the entire codebase and build Abstract Syntax Trees (ASTs).

We map upstream and downstream dependencies across files, functions, types, and modules.

We run custom lightweight language servers for multiple languages to support:

go_to_definition to find symbol declarations

find_all_references to locate usage points

fetch_signatures and fetch_types for richer semantic context

Pull Request Analysis:

When a PR is created:

We detect the diff.

We pull relevant upstream/downstream context for any changed symbols.

We gather connected function definitions, usage sites, interfaces, and infra files touched.

The LLM invokes the language servers (almost like a developer navigating manually) to reason over this structured context, not just the raw diff.

Code Quality Analysis:

Along with AI reasoning, we layer traditional static checks inside PRs:

Detecting duplicate code patterns

Finding dead, unused code blocks

Flagging overly complex functions

Goal: Make linting + AI suggestions seamless, without needing separate tools.

Security and Infrastructure Context:

We maintain an internal curated database of application security issues, mapped to OWASP and CWE.

We run Infrastructure-as-Code (IaC) security checks across:

Terraform, Kubernetes, Docker, CloudFormation, Ansible

You can optionally connect cloud accounts (AWS, GCP, Azure):

We scan your live cloud infra for misconfigurations

We pull cloud resource context into PRs (e.g., when a Terraform PR changes a live VPC rule, we show the potential blast radius).

We monitor End-of-Life (EOL) libraries and third-party package vulnerabilities by scanning the National Vulnerability Database (NVD) every 20 minutes and flagging at PR time.

In short: We try to automate how an experienced developer would actually review a change: → Understand the code structure → Understand where it’s used → Understand how infra/cloud gets affected → Catch quality, security, and complexity issues before merge — without needing extra dashboards or tools.

Teams using CodeAnt AI have reported 50%+ faster code reviews while finding deeper and more actionable problems earlier.

Would love feedback from the HN community — both technical and critical are welcome.

Thanks for checking it out!

Comments URL: https://news.ycombinator.com/item?id=43763633

Points: 1

# Comments: 0