I’ve been using AI coding tools full-time since January 2026. Not just testing them for a weekend – actually building production software with them daily.

After 3 months of real-world usage across Cursor, Claude Code, Windsurf, and GitHub Copilot, here’s what I actually think.

The TL;DR

But rankings don’t tell the real story. Let me explain.

Cursor: The Daily Driver

Cursor is where I spend 80% of my coding time. Two features make it irreplaceable:

1. Tab Completion That Actually Understands Context

Cursor doesn’t just complete the current line. It predicts what you’re about to do across multiple lines and shows it as a diff. One Tab press, and an entire function gets restructured.

This alone saves me 30-40 minutes per day. No exaggeration.

2. Agent Mode (Composer)

“Hey Cursor, add input validation to all the form handlers in this project.”

It scans the codebase, identifies the relevant files, makes the changes, and shows you a diff. You review and accept. Done.

The catch? Cursor’s credit system means heavy usage adds up. If you manually select premium models (like Claude Opus), you can burn through $60/month easily. The “Auto” mode is unlimited but uses cheaper models – which is fine 90% of the time.

Verdict: Best all-around tool if you’re willing to pay.

Claude Code: The Heavy Lifter

Claude Code is strange. It’s a terminal tool with no GUI, no autocomplete, no syntax highlighting. You type what you want in plain English, and it goes and does it.

And somehow, it’s the most reliable tool for complex tasks.

Where it shines:

I asked each tool to migrate an Express.js backend from JavaScript to TypeScript. Full migration – interfaces, type annotations, tsconfig, the works.

• Claude Code: 12 files modified correctly on the first try
• Cursor: 10/12 files, needed manual fixes
• Windsurf: 8/12 files
• Copilot: Needed 4+ rounds of prompting

The difference is Claude’s reasoning ability. It doesn’t just pattern-match – it understands what the migration requires and plans accordingly.

The catch? Rate limits are brutal. On the $20/month Pro plan, you get roughly 10-45 messages per 5-hour window. During peak hours, it’s on the lower end. I’ve been mid-refactor when the rate limit hit, and there’s nothing to do but wait.

Verdict: Best for complex, multi-file tasks. Frustrating rate limits.

Windsurf: The Underdog

Windsurf (formerly Codeium) doesn’t get enough credit.

At $15/month, it offers:

• Decent autocomplete (not Cursor-level, but solid)
• Cascade – an agent feature that automatically indexes your codebase
• A usable free tier (25 credits/month)

For 80% of daily coding tasks, Windsurf gets the job done. It won’t blow your mind like Cursor’s Tab completion, but it also won’t blow your budget.

When I use it: Side projects, smaller codebases, and when I don’t want to think about credit consumption.

Verdict: Best value. If budget matters, start here.

Copilot: The Safe Choice

GitHub Copilot is the Toyota Corolla of AI coding tools. It’s reliable, affordable ($10/month), and works inside your existing IDE.

Pros:

• Lowest friction to adopt (just install a plugin)
• Great GitHub integration (auto PR descriptions, issue linking)
• Decent autocomplete for common patterns

Cons:

• Agent mode is weaker than Cursor or Claude Code
• Less context-aware than dedicated AI editors
• Multi-file changes often need manual guidance

When I use it: When I’m in a JetBrains IDE (where Cursor isn’t available), or when I want completions without the overhead of a new editor.

Verdict: Best entry point. You’ll probably outgrow it.

What I Actually Use Day-to-Day

Here’s my real workflow:

Morning standup -> Plan tasks -> Small edits, bug fixes -> Cursor -> Large refactoring -> Claude Code -> Side project hacking -> Windsurf -> Quick PR reviews -> Copilot

I don’t think there’s a single “best” tool. The best setup is combining tools based on what each does best.

The Uncomfortable Truth About AI Coding Tools in 2026

After 3 months, here’s what nobody talks about:

1. They’re all expensive if you use them seriously.

$20/month sounds cheap until you realize that’s the base price. Heavy usage pushes Cursor to $60+, Claude Code to $100-200. That’s $1,200-2,400/year on AI coding tools alone.

2. Rate limits are the real differentiator.

The model quality across tools is converging. What actually matters day-to-day is: can I keep working without hitting a wall? Right now, Cursor (with Auto mode) and Copilot handle this best.

3. They make you faster but not necessarily better.

I ship features 2-3x faster now. But I’ve caught myself accepting AI-generated code without fully understanding it. That’s a trap. The tools should amplify your skills, not replace your thinking.

Final Scores

What’s your setup? I’m curious how others are combining these tools. Drop a comment below – especially if you’ve found a workflow that works better than mine.

This is a submission for the Notion MCP Challenge

What I Built

Study Buddy is an intelligent orchestration layer that transforms a static Notion workspace into an active learning partner.

Managing a heavy study load often involves more “organizing” than actual “learning.” Study Buddy solves this by connecting powerful LLMs (Google Gemini & Vultr Inference models) to your Notion pages via the Model Context Protocol (MCP). It acts as a proactive agent capable of:

• Synthesizing Knowledge: Answering questions based directly on your existing notes.
• Active Content Generation: Automatically creating structured study guides and active-recall quizzes from scratch.
• Multi-Channel Interaction: Accessible via a high-performance CLI for deep work or a private Telegram Bot for on-the-go learning.
• Enterprise-Grade Security: A custom implementation of OAuth 2.0 + PKCE with Dynamic Client Registration ensures your data remains yours.

Video Demo

Show us the code

GitHub Repository: Study Buddy MCP Client

How I Used Notion MCP

I utilized the Notion MCP Server as the standardized “nervous system” of the project. Instead of hardcoding API endpoints, I built a dynamic Orchestration Loop:

1. Reasoning: Using Google Gemini, the system interprets user intent (e.g., “Summarize my last lecture”).
2. Tool Discovery: The client queries the MCP server to discover available capabilities (searching pages, creating content, appending blocks).
3. Autonomous Execution: The model emits tool_calls in strict JSON. My client executes these against the MCP server, feeds the results back to the LLM, and iterates until the study task is complete.
4. Resilient Auth: I implemented a robust OAuth 2.0 + PKCE flow. By using MCP, the client can discover resource metadata and handle token refreshes automatically, providing a seamless “plug-and-play” experience for any Notion user.

By abstracting Notion’s complexity into a ‘Zero-UI’ experience, Study Buddy handles the friction of database management and content structuring via simple chat, allowing students to spend less time ‘organizing’ and more time mastering their material.

Every enterprise asking about LLM fine-tuning has the same question: “Should we fine-tune, use RAG, or just improve our prompts?” The answer depends on your task, data, budget, latency requirements, and security posture. Yet no guide on Google provides a clear decision framework — Unsloth sells its tool, Lakera sells security, DataCamp sells courses.

This guide synthesizes the technical depth of Unsloth, the security perspective of Lakera, and the academic rigor of the arXiv comprehensive survey — with an enterprise decision framework and cost analysis that none of them provide.

What Is Fine-Tuning? And Why It Matters for Enterprises

LLM fine-tuning is the process of taking a pre-trained language model and re-training it on domain-specific data to customize its behavior. It’s a subset of transfer learning: you leverage the model’s existing knowledge and adapt it to your use case.

Why it matters now: enterprises are paying $16-21 CPC for fine-tuning expertise — among the highest CPCs in the entire AI keyword space. The demand is real, the expertise is scarce.

Fine-Tuning vs RAG vs Prompting: The Decision Framework

The framework no competitor provides:

Practical rule:

• Need the model to know updated information? → RAG
• Need the model to behave a specific way? → Fine-tuning
• Need both? → RAG + fine-tuning (the most powerful combination)

Unsloth’s controversial claim: Fine-tuning can replicate ALL RAG capabilities. This is technically possible (train the model on your documents) but impractical for most enterprises — knowledge changes frequently, and re-training is slower than updating a RAG index. The claim holds for static, specialized knowledge; it fails for dynamic, frequently updated content.

Fine-Tuning Methods in 2026

Core Methods

Alignment Methods

LoRA is the breakthrough that democratized fine-tuning: by training only 1% of model weights, it reduces GPU/VRAM needs by 10-100x. QLoRA takes it further — quantizing to 4 bits enables fine-tuning 65B+ parameter models on a single consumer GPU with just 3GB VRAM (Unsloth).

Choosing a Model for Fine-Tuning (2026)

Real-world experience: DeepSeek failed (generated Chinese characters), Llama failed, Mistral 7B succeeded in a practical fine-tuning project. Lesson: always test 2-3 models before committing.

Fine-Tuning Tools and Frameworks

How Much Does Fine-Tuning Cost?

Where to train:

Security Risks: Data Poisoning, Prompt Injection, and Model Extraction

Lakera highlights critical security concerns that most fine-tuning guides ignore:

Dropbox uses Lakera Guard for LLM security with their fine-tuned models. If you’re fine-tuning with proprietary or sensitive data, security isn’t optional — it’s foundational.

Fine-Tuning for Blockchain and Web3

At Beltsys, we apply LLM fine-tuning for Web3 use cases:

• Models trained on Solidity for smart contract generation and auditing
• LLMs specialized in ERC-3643, ERC-4337 documentation and tokenization standards
• RAG + fine-tuned chatbots for Web3 platform technical support
• Fine-tuned agents for on-chain transaction analysis and DeFi protocol interaction

The combination of fine-tuning + RAG is ideal for fintechs and blockchain companies that need models speaking their technical language with current data. Blockchain & AI consulting.

EU AI Act and Fine-Tuned Models

The EU AI Act raises an unresolved question: is a fine-tuned model a “new” AI system?

• Substantial behavior modification → may classify as new system → mandatory compliance
• Minor adaptation (tone/format) → probably not
• Recommendation: Document the fine-tuning process, training data, and evaluations. If your model makes decisions in healthcare, finance, or hiring, assume you need compliance.
• Deadline: August 2, 2026. Penalties: up to €35M or 7% of global revenue.

Frequently Asked Questions About LLM Fine-Tuning

What is LLM fine-tuning?

LLM fine-tuning is the process of re-training a pre-trained language model with domain-specific data to customize its behavior. It’s a subset of transfer learning — you leverage existing knowledge and adapt it to your use case. Key techniques include LoRA (trains 1% of weights), QLoRA (4-bit quantization for consumer GPUs), and DPO (alignment without reward model).

When should I fine-tune vs use RAG?

Fine-tune when you need the model to behave a specific way (tone, format, specialized responses). Use RAG when you need the model to know updated information (documentation, FAQs). Use both for maximum customization with current knowledge. Fine-tuning is better for static, specialized knowledge; RAG for dynamic content.

How much does LLM fine-tuning cost?

A 7B model with LoRA: $10-100 in GPU costs (2-4 hours). A 70B model with QLoRA: $50-500. Monthly hosting: $50-1,000 depending on model size. Free options: Google Colab, Kaggle (30h/week GPU). Compared to API costs ($500-5,000+/month), fine-tuning is cheaper long-term and keeps data on-premise.

What is LoRA and why does it matter?

LoRA (Low-Rank Adaptation) trains only adapter layers — approximately 1% of model weights — reducing GPU/VRAM requirements by 10-100x. QLoRA adds 4-bit quantization, enabling fine-tuning of 65B+ parameter models on a single consumer GPU with just 3GB VRAM. These techniques democratized fine-tuning for enterprises of all sizes.

Is fine-tuning secure?

Not automatically. Lakera warns that fine-tuned models remain vulnerable to prompt injection, data poisoning, training data leakage, and model extraction attacks. Dropbox uses Lakera Guard for LLM security. If fine-tuning with proprietary data: implement data validation, differential privacy, input sanitization, and adversarial testing.

Does the EU AI Act apply to fine-tuned models?

Potentially. If fine-tuning substantially modifies model behavior, it may create a “new” AI system requiring compliance. For models making decisions in healthcare, finance, or hiring, assume compliance is needed. Document training data, process, and evaluations. Deadline: August 2, 2026. Penalties: €35M or 7% revenue.

About the Author

Beltsys is a Spanish blockchain and AI development company specializing in LLM fine-tuning for Web3, smart contracts, and fintech solutions. With extensive experience across more than 300 projects since 2016, Beltsys implements custom models with RAG and fine-tuning for enterprises that need AI speaking their technical language. Learn more about Beltsys

Related: Smart Contract Development
Related: Web3 Development
Related: Blockchain Consulting
Related: Real Estate Tokenization

How to Build an Earnings Call Transcript Analyzer

Earnings calls contain signals that move stock prices — hedging language, forward guidance changes, and sentiment shifts. Most investors read transcripts manually. We’ll build a Python tool that scrapes transcripts and extracts actionable signals automatically.

The Value of Automated Transcript Analysis

When a CEO shifts from “we expect strong growth” to “we anticipate headwinds,” that language change predicts stock movement. NLP can detect these shifts across hundreds of companies simultaneously — something no human analyst can do.

Scraping Earnings Transcripts

Several sites publish free earnings call transcripts. We’ll build a scraper that collects them systematically using ScraperAPI for reliable proxy rotation:

import requests
from bs4 import BeautifulSoup
import re

SCRAPER_API_KEY = "YOUR_KEY"

def scrape_transcript(url):
    response = requests.get(
        "http://api.scraperapi.com",
        params={
            "api_key": SCRAPER_API_KEY,
            "url": url,
            "render": "true"
        },
        timeout=60
    )
    soup = BeautifulSoup(response.text, "html.parser")
    segments = []
    current_speaker = None
    current_text = []
    current_role = ""

    for element in soup.find_all(["p", "strong", "b"]):
        text = element.get_text(strip=True)
        speaker_match = re.match(r"^([A-Z][a-z]+ [A-Z][a-z]+)s*[-u2014]s*(.+)", text)
        if speaker_match:
            if current_speaker:
                segments.append({
                    "speaker": current_speaker,
                    "role": current_role,
                    "text": " ".join(current_text)
                })
            current_speaker = speaker_match.group(1)
            current_role = speaker_match.group(2)
            current_text = []
        elif current_speaker:
            current_text.append(text)
    return segments

Sentiment Analysis Engine

We analyze sentiment at the sentence level to catch nuanced shifts:

from collections import Counter
import re

POSITIVE_TERMS = {
    "growth", "exceeded", "strong", "record", "confident",
    "momentum", "opportunity", "upside", "robust", "accelerating",
    "outperformed", "beat", "raised", "guidance", "optimistic"
}

NEGATIVE_TERMS = {
    "headwinds", "challenging", "decline", "pressure", "uncertainty",
    "cautious", "softness", "deceleration", "missed", "below",
    "restructuring", "impairment", "risk", "concern", "weakness"
}

HEDGING_TERMS = {
    "may", "might", "could", "potentially", "possibly",
    "approximately", "somewhat", "relatively", "generally"
}

def analyze_segment(segment):
    words = set(re.findall(r'bw+b', segment["text"].lower()))
    pos_count = len(words & POSITIVE_TERMS)
    neg_count = len(words & NEGATIVE_TERMS)
    hedge_count = len(words & HEDGING_TERMS)
    total = pos_count + neg_count or 1
    sentiment_score = (pos_count - neg_count) / total

    return {
        "speaker": segment["speaker"],
        "role": segment["role"],
        "sentiment": sentiment_score,
        "hedging_ratio": hedge_count / max(len(words), 1),
        "positive_terms": list(words & POSITIVE_TERMS),
        "negative_terms": list(words & NEGATIVE_TERMS),
        "word_count": len(words)
    }

Forward Guidance Detection

Extract and compare forward-looking statements:

def extract_guidance(segments):
    guidance_patterns = [
        r"(?:we expect|we anticipate|we project|we forecast|guidance).{10,200}",
        r"(?:next quarter|full year|fiscal year).{10,200}",
        r"(?:revenue.{0,30}(?:between|range|expect)|eps.{0,30}(?:between|range|expect)).{10,100}",
    ]
    guidance_statements = []
    for segment in segments:
        if segment.get("role", "").lower() in ["ceo", "cfo", "chief executive", "chief financial"]:
            for pattern in guidance_patterns:
                matches = re.findall(pattern, segment["text"].lower())
                for match in matches:
                    guidance_statements.append({
                        "speaker": segment["speaker"],
                        "role": segment["role"],
                        "statement": match.strip(),
                        "type": "forward_guidance"
                    })
    return guidance_statements

Quarter-over-Quarter Comparison

The real value emerges when you compare transcripts across quarters:

def compare_quarters(current_analysis, previous_analysis):
    changes = {}
    for speaker in set(a["speaker"] for a in current_analysis):
        curr = [a for a in current_analysis if a["speaker"] == speaker]
        prev = [a for a in previous_analysis if a["speaker"] == speaker]
        if curr and prev:
            sentiment_shift = curr[0]["sentiment"] - prev[0]["sentiment"]
            hedging_shift = curr[0]["hedging_ratio"] - prev[0]["hedging_ratio"]
            changes[speaker] = {
                "sentiment_change": round(sentiment_shift, 3),
                "hedging_change": round(hedging_shift, 3),
                "signal": (
                    "BEARISH" if sentiment_shift < -0.2 or hedging_shift > 0.05
                    else "BULLISH" if sentiment_shift > 0.2
                    else "NEUTRAL"
                )
            }
    return changes

Scaling the Analysis

To analyze hundreds of earnings calls per quarter, you need robust proxy infrastructure. ThorData provides residential proxies for scraping financial sites, and ScrapeOps monitors scraper performance across sources.

Putting It All Together

def full_analysis_pipeline(transcript_url):
    segments = scrape_transcript(transcript_url)
    analysis = [analyze_segment(s) for s in segments]
    guidance = extract_guidance(segments)
    ceo_sentiment = [a for a in analysis if "ceo" in a["role"].lower()]
    overall = sum(a["sentiment"] for a in analysis) / len(analysis) if analysis else 0
    return {
        "overall_sentiment": round(overall, 3),
        "ceo_sentiment": ceo_sentiment,
        "guidance_statements": guidance,
        "segment_count": len(segments)
    }

Key Insights

• Hedging increases in the Q&A section often predict earnings misses next quarter
• CEO vs CFO sentiment divergence can signal internal disagreements about outlook
• Word count changes (shorter answers) sometimes indicate discomfort with a topic

Build this analyzer, run it on historical transcripts, and backtest against stock performance. The patterns are real and exploitable.

Let me describe a scene every LaTeX user has lived:

It’s 11pm. You’ve been writing all day. You hit compile, walk away to make tea, come back to find this in the log:

! Package inputenc Error: Unicode character 的 (U+7684)
(inputenc)                not set up for use with LaTeX.

See the inputenc package documentation for explanation.
Type  H <return>  for immediate help.
 ...
l.1847 ...rithm based on the work of Li et al. 的

?

And you just stare at it. You didn’t even type a Chinese character. Where did that come from? Is it in a bibliography entry? A comment someone else left? A package that auto-inserted something?

This is the specific kind of LaTeX suffering that has no good solution — until recently.

The problem with current LaTeX debugging tools

When your compile log hits 400 lines, the challenge isn’t understanding individual error messages. It’s triage — figuring out which errors are root causes and which ones are just cascading side effects of a single upstream problem.

I used to do this manually. Open the log. Search for !. Copy the error. Google it. Read StackOverflow answers that almost match my situation. Try a fix. Recompile. Different error. Repeat.

Calling ChatGPT helps a little. But here’s the issue: ChatGPT doesn’t know your codebase. You paste a snippet, it gives you a generic answer. It doesn’t know what packages you’ve loaded, what custom commands you’ve defined, or what your root file structure looks like. So you get answers like “make sure amsmath is loaded” when you already have amsmath loaded and the real problem is something three files upstream.

What TeX64’s Axiom does differently

TeX64 is a local-first LaTeX editor for macOS. Its AI assistant, Axiom, has access to your entire project — all .tex files, your actual compile log, and your BibTeX. Not a snippet you pasted. The whole thing.

This changes the quality of the answers dramatically.

Here’s a real example. I had a project with 23 errors in the log. I typed “analyze these errors” into Axiom’s chat. The response:

“You have 23 errors but they stem from 3 root causes:

Root cause 1 (generates 18 errors): macros.tex line 34 defines proof using newcommand, but the amsthm package already defines a proof environment. This causes a naming conflict that cascades throughout the document.

Root cause 2 (generates 4 errors): chapter4.tex line 209 has a begin{table} with no matching end{table} — LaTeX loses track of the environment and the errors pile up from there.

Root cause 3 (1 error): bibliography.bib entry Zhang2019 has a { in the title field that’s not closed.

I’ll show diffs for each fix. Want to start with root cause 1?”

That analysis would have taken me an hour of manual log reading. It took Axiom about 20 seconds.

The diff-before-apply approach

One thing Axiom does that I deeply appreciate: it shows you exactly what it wants to change before changing anything.

Every suggestion comes with a diff:

  % macros.tex, line 34
- newcommand{proof}[1]{textit{Proof.} #1 qed}
+ newcommand{myproof}[1]{textit{Proof.} #1 qed}

And then: “Apply this change?”

You see what’s happening, you understand why, and you decide. Axiom never silently edits your files. For a thesis or paper where every word matters, this is the right way to work with AI.

Other things I’ve used it for

Once I started using TeX64, I found Axiom useful for things beyond error fixing:

Generating math I don’t remember the syntax for: I can describe a formula in plain English — “the limit as n approaches infinity of the sum from k=1 to n of 1/k^2” — and get clean LaTeX back. No more digging through the LaTeX math symbol list.

Literature: paste title, get BibTeX: Tell Axiom you want to cite a paper, it searches arXiv and drops the BibTeX entry into your .bib file. This alone has saved me probably 20 minutes per writing session.

Package conflict diagnosis: When adding packages to a big project causes mysterious new errors, Axiom can spot ordering conflicts. “You’re loading cleveref before hyperref — switch the order.”

Honest limitations

TeX64 is macOS only. If you’re on Windows or Linux, this isn’t for you.

You need MacTeX or TeX Live installed separately — TeX64 is the editor, not a LaTeX distribution. The app’s environment diagnostics will walk you through setup if you don’t have it already.

AI features require internet. Compilation itself is fully local and offline-capable. On my M2 MacBook Air, a 120-page document compiles in about 8 seconds — no internet bottleneck on the compile step.

There’s a free tier to start with. If you end up using Axiom heavily, there are paid plans.

The thing that surprised me most

I expected Axiom to be useful. What I didn’t expect was how much it changed my relationship with errors.

Before: compile error → dread → 45-minute debugging session.

After: compile error → “let Axiom look at this” → usually fixed in under 5 minutes.

The psychological difference is real. When you’re not scared of errors, you compile more often. When you compile more often, you catch problems earlier. It’s a virtuous loop that actually makes you write more.

If you’re on a Mac and spend meaningful time in LaTeX, it’s worth trying.

→ tex64.com

Google’s Cache Compression, Siri’s Open Door, and the CFO Agent Test

Google slashes AI memory needs by 6x while Apple opens Siri to rivals, and a new AI-focused language arrives as LLM agents face their first CFO benchmark.

Google’s TurboQuant reduces AI LLM cache memory capacity requirements by at least six times — up to 8x performance boost on Nvidia H100 GPUs, compresses KV caches to 3 bits with no accuracy loss – Tom

What happened:

Google’s TurboQuant cuts LLM cache memory requirements by at least six times, achieving up to 8x performance gains on Nvidia H100 GPUs by compressing KV caches to just 3 bits without accuracy loss.

Why it matters:

This breakthrough directly addresses the memory bottleneck that limits LLM inference speed and scale, potentially enabling larger models to run on existing hardware or dramatically reducing infrastructure costs for AI services.

Context:

KV cache compression has been a key focus area as models grow larger and inference costs become prohibitive for many applications.

Apple Plans to Open Up Siri to Rival AI Assistants in iOS 27 Update

What happened:

Apple plans to allow Siri to integrate with competing AI assistants beyond ChatGPT in the upcoming iOS 27 update, according to Bloomberg.

Why it matters:

This marks a significant shift from Apple’s traditionally closed ecosystem, potentially giving developers and users more flexibility while forcing Apple to compete on AI quality rather than lock-in.

Context:

The move comes as Apple faces pressure to keep pace with rapidly advancing AI capabilities from competitors like OpenAI and Google.

Aria – A programming language specifically for AI code generation

What happened:

Aria is a new programming language designed specifically for AI code generation, with its website and documentation now available online.

Why it matters:

Purpose-built languages for AI development could streamline workflows and reduce the friction between human intent and machine-generated code, potentially becoming a standard tool for AI-assisted programming.

Context:

As AI coding tools mature, specialized languages may emerge to bridge the gap between natural language prompts and executable code.

OpenAI Backs Isara, New AI Startup Seeking Bot Army Breakthroughs

What happened:

OpenAI has backed Isara, a new AI startup focused on developing breakthroughs for bot army technology, according to the Wall Street Journal.

Why it matters:

This investment signals OpenAI’s interest in scaling AI agents beyond single-task assistants toward coordinated multi-agent systems, which could transform everything from customer service to software testing.

Context:

The “bot army” concept represents the next frontier in agentic AI, where multiple specialized agents work together autonomously.

Software Engineer Interviews for the Age of AI

What happened:

A blog post explores how software engineer interviews should evolve in the age of AI, with discussion on Hacker News.

Why it matters:

As AI tools become ubiquitous in development, traditional coding interviews may need to test problem-solving and system design skills rather than memorization or manual coding speed.

Context:

Companies are grappling with how to evaluate engineers when AI can handle much of the routine coding work.

Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments

What happened:

Researchers have created a benchmark testing whether LLM agents can handle CFO-level resource allocation decisions in dynamic enterprise environments, addressing uncertainty and competing objectives.

Why it matters:

This benchmark tackles one of AI’s biggest challenges: making strategic, long-term decisions with incomplete information—a capability that could transform business operations if achieved.

Context:

While LLMs excel at reactive tasks, complex resource allocation requires planning and trade-offs that remain difficult for current AI systems.

Sources: Google News AI, Hacker News AI, Arxiv AI

Building a Real Estate Foreclosure Tracker with Public Records

Foreclosure data is public information in the United States, but it’s scattered across county clerk websites, court systems, and government databases. Investors, researchers, and journalists who can aggregate this data gain a significant informational advantage. Let’s build a Python-based foreclosure tracker.

Why Track Foreclosures?

Foreclosure filings are leading indicators of economic stress in specific markets. They help real estate investors find below-market properties, help journalists investigate predatory lending, and help researchers study housing market dynamics.

Data Sources

Foreclosure data comes from several public sources:

• County clerk/recorder websites — Notice of Default, Lis Pendens filings
• HUD foreclosure listings — government-owned properties
• Court records — judicial foreclosure proceedings
• Census/ACS data — demographic context

HUD Foreclosure Listings Scraper

import requests
from bs4 import BeautifulSoup
import re

SCRAPER_API_KEY = "YOUR_KEY"

class HUDForeclosureScraper:
    BASE_URL = "https://www.hudhomestore.gov"

    def search_properties(self, state, city=None, zip_code=None):
        search_url = f"{self.BASE_URL}/Listing/PropertySearchResult"
        form_data = {
            "State": state,
            "City": city or "",
            "Zip": zip_code or "",
            "PropertyType": "ALL",
            "ListingStatus": "Available"
        }
        response = requests.post(
            "http://api.scraperapi.com",
            params={"api_key": SCRAPER_API_KEY, "url": search_url, "render": "true"},
            data=form_data,
            timeout=60
        )
        return self._parse_listings(response.text)

    def _parse_listings(self, html):
        soup = BeautifulSoup(html, "html.parser")
        properties = []
        for listing in soup.select(".property-listing, .listing-row, tr"):
            address = listing.select_one(".address, td:nth-child(1)")
            price = listing.select_one(".price, td:nth-child(3)")
            status = listing.select_one(".status, td:nth-child(4)")
            if address:
                properties.append({
                    "address": address.get_text(strip=True),
                    "price": price.get_text(strip=True) if price else "N/A",
                    "status": status.get_text(strip=True) if status else "N/A"
                })
        return properties

County Records Scraper

class CountyRecordsScraper:
    def __init__(self, scraper_api_key):
        self.api_key = scraper_api_key

    def scrape_county_records(self, county_url, search_params):
        response = requests.get(
            "http://api.scraperapi.com",
            params={"api_key": self.api_key, "url": county_url, "render": "true"},
            timeout=60
        )
        soup = BeautifulSoup(response.text, "html.parser")
        filings = []
        tables = soup.find_all("table")
        for table in tables:
            rows = table.find_all("tr")
            for row in rows[1:]:
                cells = row.find_all("td")
                if len(cells) >= 4:
                    filing = {
                        "case_number": cells[0].get_text(strip=True),
                        "filing_date": cells[1].get_text(strip=True),
                        "document_type": cells[2].get_text(strip=True),
                        "property_address": cells[3].get_text(strip=True)
                    }
                    foreclosure_types = [
                        "notice of default", "lis pendens",
                        "notice of sale", "foreclosure"
                    ]
                    if any(ft in filing["document_type"].lower() for ft in foreclosure_types):
                        filings.append(filing)
        return filings

Geocoding and Market Analysis

import time

def geocode_address(address):
    response = requests.get(
        "https://nominatim.openstreetmap.org/search",
        params={"q": address, "format": "json", "limit": 1},
        headers={"User-Agent": "ForeclosureTracker/1.0"}
    )
    time.sleep(1)
    results = response.json()
    if results:
        return {
            "lat": float(results[0]["lat"]),
            "lon": float(results[0]["lon"]),
            "display_name": results[0]["display_name"]
        }
    return None

def market_concentration(properties):
    from collections import Counter
    zips = []
    for prop in properties:
        zip_match = re.search(r'b(d{5})b', prop.get("address", ""))
        if zip_match:
            zips.append(zip_match.group(1))
    concentration = Counter(zips).most_common(20)
    return [{"zip": z, "count": c} for z, c in concentration]

Building the Pipeline

import pandas as pd
from datetime import datetime

class ForeclosureTracker:
    def __init__(self, api_key):
        self.hud = HUDForeclosureScraper()
        self.county = CountyRecordsScraper(api_key)

    def daily_scan(self, states, county_urls):
        all_properties = []
        for state in states:
            properties = self.hud.search_properties(state)
            for p in properties:
                p["source"] = "HUD"
                p["state"] = state
            all_properties.extend(properties)
        for url in county_urls:
            filings = self.county.scrape_county_records(url, {})
            for f in filings:
                f["source"] = "County"
            all_properties.extend(filings)
        df = pd.DataFrame(all_properties)
        df["scan_date"] = datetime.now().isoformat()
        filename = f"foreclosures_{datetime.now():%Y%m%d}.csv"
        df.to_csv(filename, index=False)
        print(f"Found {len(all_properties)} properties across {len(states)} states")
        return df

Scaling Across Counties

With over 3,000 counties in the US, scaling requires solid proxy infrastructure. ScraperAPI handles JavaScript rendering for modern county sites. ThorData residential proxies prevent IP blocks during large scans. ScrapeOps tracks success rates per county.

Legal Considerations

All data scraped here is public record. However, respect rate limits, comply with each site’s terms of service, and avoid overloading government infrastructure. This tool is designed for legitimate research, journalism, and investment analysis.

Track foreclosures systematically and you’ll see market shifts before they appear in the headlines.

How to Scrape Conference Speaker Lineups for Trend Detection

Conference speaker lineups are a leading indicator of industry trends. When multiple conferences simultaneously feature talks on a topic, it signals emerging demand months before mainstream adoption. Let’s build a scraper that tracks speaker lineups across tech conferences and identifies trending topics.

Why Conference Data Matters

By the time a topic appears in a Gartner report, it’s already mainstream. Conference organizers curate lineups 3-6 months ahead based on what’s gaining traction. Tracking these patterns gives you an early warning system for industry trends.

Building the Conference Scraper

import requests
from bs4 import BeautifulSoup
import re
from urllib.parse import urljoin

SCRAPER_API_KEY = "YOUR_KEY"

class ConferenceScraper:
    def scrape_speakers(self, conference_url):
        response = requests.get(
            "http://api.scraperapi.com",
            params={
                "api_key": SCRAPER_API_KEY,
                "url": conference_url,
                "render": "true"
            },
            timeout=60
        )
        soup = BeautifulSoup(response.text, "html.parser")
        speaker_links = []
        for link in soup.find_all("a", href=True):
            href = link["href"].lower()
            text = link.get_text().lower()
            if any(kw in href or kw in text for kw in ["speaker", "schedule", "agenda", "session"]):
                full_url = urljoin(conference_url, link["href"])
                speaker_links.append(full_url)

        speakers = []
        for page_url in set(speaker_links[:5]):
            page_speakers = self._scrape_speaker_page(page_url)
            speakers.extend(page_speakers)
        return speakers

    def _scrape_speaker_page(self, url):
        response = requests.get(
            "http://api.scraperapi.com",
            params={"api_key": SCRAPER_API_KEY, "url": url, "render": "true"},
            timeout=60
        )
        soup = BeautifulSoup(response.text, "html.parser")
        speakers = []
        cards = soup.select(
            ".speaker-card, .speaker-item, .speaker, "
            "[class*='speaker'], [class*='presenter']"
        )
        for card in cards:
            name_el = card.select_one("h2, h3, h4, .speaker-name, .name")
            title_el = card.select_one(".title, .role, .position, .company")
            talk_el = card.select_one(".talk-title, .session-title, .topic")
            if name_el:
                speakers.append({
                    "name": name_el.get_text(strip=True),
                    "title": title_el.get_text(strip=True) if title_el else "",
                    "talk": talk_el.get_text(strip=True) if talk_el else "",
                    "source_url": url
                })
        return speakers

Topic Extraction and Clustering

from sklearn.feature_extraction.text import TfidfVectorizer
from collections import Counter
import numpy as np

class TopicAnalyzer:
    STOP_WORDS = {
        "talk", "session", "keynote", "workshop", "panel",
        "introduction", "building", "using", "deep", "dive"
    }

    def extract_topics(self, speakers):
        talks = [s["talk"] for s in speakers if s.get("talk")]
        if not talks:
            return []
        vectorizer = TfidfVectorizer(
            max_features=1000, stop_words="english",
            ngram_range=(1, 3), min_df=2
        )
        tfidf = vectorizer.fit_transform(talks)
        feature_names = vectorizer.get_feature_names_out()
        mean_scores = np.asarray(tfidf.mean(axis=0)).flatten()
        top_indices = mean_scores.argsort()[-30:][::-1]
        topics = []
        for idx in top_indices:
            term = feature_names[idx]
            if term.lower() not in self.STOP_WORDS:
                topics.append({
                    "term": term,
                    "score": float(mean_scores[idx]),
                    "frequency": int((tfidf[:, idx] > 0).sum())
                })
        return topics

    def detect_trends(self, current_topics, previous_topics):
        current_dict = {t["term"]: t["score"] for t in current_topics}
        previous_dict = {t["term"]: t["score"] for t in previous_topics}
        trends = []
        for term, score in current_dict.items():
            prev_score = previous_dict.get(term, 0)
            if prev_score == 0 and score > 0.01:
                trends.append({"term": term, "type": "NEW", "score": score})
            elif score > prev_score * 1.5:
                growth = (score - prev_score) / max(prev_score, 0.001)
                trends.append({"term": term, "type": "GROWING", "growth": round(growth, 2)})
        return sorted(trends, key=lambda x: x.get("score", x.get("growth", 0)), reverse=True)

Cross-Conference Analysis

def cross_conference_analysis(conference_data):
    topic_conference_map = {}
    for conf_name, speakers in conference_data.items():
        analyzer = TopicAnalyzer()
        topics = analyzer.extract_topics(speakers)
        for topic in topics:
            term = topic["term"]
            if term not in topic_conference_map:
                topic_conference_map[term] = []
            topic_conference_map[term].append({
                "conference": conf_name,
                "score": topic["score"]
            })
    trending = {
        term: confs for term, confs in topic_conference_map.items()
        if len(confs) >= 3
    }
    return dict(sorted(trending.items(), key=lambda x: len(x[1]), reverse=True))

Scaling with Proxy Infrastructure

Scraping dozens of conference sites requires reliable proxies. ScraperAPI handles JavaScript-rendered conference pages. For geo-specific conferences, ThorData residential proxies work well. ScrapeOps monitors scraping health.

Running the Full Pipeline

conferences = {
    "PyCon 2026": "https://us.pycon.org/2026/schedule/",
    "KubeCon EU": "https://events.linuxfoundation.org/kubecon/",
    "AWS re:Invent": "https://reinvent.awsevents.com/sessions/"
}

scraper = ConferenceScraper()
all_data = {}
for name, url in conferences.items():
    speakers = scraper.scrape_speakers(url)
    all_data[name] = speakers
    print(f"{name}: {len(speakers)} speakers")

trends = cross_conference_analysis(all_data)
for topic, confs in list(trends.items())[:10]:
    print(f"TRENDING: {topic} ({len(confs)} conferences)")

Conference lineups are one of the most underutilized data sources in tech. Start tracking them systematically and you’ll spot trends months before they hit mainstream awareness.