# Aniket Aslaliya - LLM Content Index (Full)

Canonical site: https://aniketaslaliya.dev
Primary blog index: https://aniketaslaliya.dev/blog
Sitemap: https://aniketaslaliya.dev/sitemap.xml
LLM sitemap: https://aniketaslaliya.dev/llm-sitemap.xml

## Full Post Index

### Claude Opus 4.7 and the Delegation Threshold for High-Trust Work
URL: https://aniketaslaliya.dev/blog/claude-opus-4-7-delegation-threshold
Published: 2026-04-16
Updated: 2026-04-16
Category: AI Product Strategy
Tags: Claude, AI Workflows, Product Strategy, Engineering, Finance
An executive-grade analysis of Claude Opus 4.7 focused on practical delegation: where supervision can be reduced, where verification remains essential, and how teams should redesign execution loops.
Key points:
- Opus 4.7 is best interpreted as a delegation threshold, not a benchmark trophy.
- The biggest gain is multi-step execution with self-checking behavior over longer runs.
- Teams that redesign workflow now will compound output without compounding review fatigue.
References:
- Anthropic: Introducing Claude Opus 4.7: https://www.anthropic.com/news/claude-opus-4-7
- Terminal-Bench: benchmark for terminal agent mastery: https://www.tbench.ai/
- SWE-bench: software engineering agent evaluation: https://www.swebench.com/
- Anthropic Responsible Scaling Policy (risk governance context): https://www.anthropic.com/news/announcing-our-updated-responsible-scaling-policy

### The PM's guide to evaluating LLM outputs: beyond vibes to real metrics
URL: https://aniketaslaliya.dev/blog/pm-guide-llm-evals-beyond-vibes
Published: 2026-04-16
Updated: 2026-04-16
Category: AI Product Ops
Tags: LLM Evals, Product Metrics, AI Reliability
LLM products fail quietly when teams rely only on intuition. This guide shows how to design lightweight, high-signal eval loops.
Key points:
- Define quality as task success, not just fluent output.
- Track latency and unit economics alongside hallucination and refusal rates.
- Use release gates tied to thresholds so quality does not regress silently.

### I analyzed 15 startup hiring posts - most are not what they seem
URL: https://aniketaslaliya.dev/blog/startup-hiring-posts-pattern-analysis
Published: 2026-04-13
Updated: 2026-04-13
Category: Career / Hiring
Tags: Hiring, Students, Career Strategy, Founders, Internships
A balanced, pattern-based analysis of startup hiring posts that surfaces fake urgency, unclear role design, and compensation ambiguity, plus a practical verification checklist students can apply in minutes.
Key points:
- Many startup hiring posts optimize for attention before role clarity, creating confusion for candidates.
- The biggest risks are not always obvious scams; they are low-transparency funnels with weak role definition.
- A simple legitimacy checklist can save candidates from weeks of wasted effort and misaligned offers.
References:
- FTC: Job Scams - warning signs and verification guidance: https://consumer.ftc.gov/articles/job-scams
- LinkedIn: How to spot job scams and suspicious recruiter patterns: https://www.linkedin.com/help/linkedin/answer/a1340187

### How I Design AI Systems - The Framework That Won Me a Google Cloud Gen AI Exchange Hackathon
URL: https://aniketaslaliya.dev/blog/ai-systems-from-prompt-to-product
Published: 2026-04-13
Updated: 2026-04-13
Category: AI Product Systems
Tags: AI Systems, RAG, Product Management, LLM Engineering, Evaluation
A practical framework for building AI products as systems, grounded in what worked and failed while building Legal SahAI for the Google Cloud Gen AI Exchange Hackathon.
Key points:
- Prompts do not fail in isolation; systems fail when retrieval, evaluation, and feedback are missing.
- The highest leverage comes from designing tight interfaces between intent, context, generation, and quality checks.
- Great AI products are iteration machines with measurable quality, latency, and cost targets.
References:
- Google Cloud: Retrieval-augmented generation (RAG) overview: https://cloud.google.com/use-cases/retrieval-augmented-generation
- NIST AI Risk Management Framework (AI RMF 1.0): https://www.nist.gov/itl/ai-risk-management-framework

### I Got a Rs45K Product Management Offer Without an Interview - A Hiring Reality Check
URL: https://aniketaslaliya.dev/blog/zorvyn-hiring-analysis
Published: 2026-04-12
Updated: 2026-04-12
Category: Career / Hiring
Tags: Hiring, Internships, Scam Awareness, Product Management, Students
A first-person hiring reality check on receiving a high-stipend PM offer without interviews, plus a practical framework to verify offer legitimacy.
Key points:
- I received a Rs45K PM internship offer without interviews, case rounds, or team interaction.
- A changing or unclear domain structure during active hiring adds an extra verification burden for candidates.
- This is not an accusation; it is a signal-based analysis and a practical due-diligence framework for students.
References:
- FTC: Job Scams (consumer guidance): https://consumer.ftc.gov/articles/job-scams

### Anthropic Mythos: how dangerous could a cyber-capable AI model become?
URL: https://aniketaslaliya.dev/blog/anthropic-mythos-model-risk
Published: 2026-04-10
Updated: 2026-04-10
Category: AI Safety
Tags: AI Safety, Cybersecurity, Anthropic
A grounded take on why Anthropic's Mythos Preview could become dangerous if access, monitoring, evaluation, and deployment controls are not handled with extreme care.
Key points:
- Cyber-capable AI is dual-use by default, so release strategy matters as much as model quality.
- Risk grows when strong models are connected to tools and workflows without strict control boundaries.
- Responsible deployment needs identity checks, logging, red-teaming, rate limits, and clear incident response.

Generated: 2026-04-16