2026-02-14 12:00:39
The hot take everyone's avoiding: AI coding assistants are making developers "lazy" — and we should celebrate it.
I'm watching developers panic about Copilot, Cursor, and the latest AI coding tools "doing the work for them." The fear is real: Will I forget how to code? Am I becoming dependent? What if the AI writes bad code?
Wrong questions. Here are the right ones.
Remember when developers worried that high-level languages would make them "forget" assembly? That IDEs with autocomplete would make them "forget" syntax? That Stack Overflow would make them "forget" how to problem-solve?
Each abstraction layer didn't make developers dumber. It made them focus on what actually matters.
Assembly → C → Python → frameworks → AI assistance. Same ladder, higher rung.
When developers say AI makes them lazy, they usually mean:
That's not lazy. That's efficient.
The best developers were always "lazy" in this sense. They automated repetitive tasks, built reusable components, and focused brain cycles on architecture and business logic.
AI coding assistants just democratized that efficiency.
Here's what AI can't do (yet):
If you're spending 60% of your time on syntax and boilerplate, AI frees you to spend 80% on these higher-value skills.
"But what if the AI goes down?"
What if Stack Overflow goes down? What if GitHub goes down? What if your IDE crashes?
We're already dependent on dozens of tools. Adding one more intelligent tool to the stack isn't fundamentally different.
The smart move isn't avoiding dependency — it's understanding your tools deeply enough to work without them when needed.
Stop feeling guilty about AI assistance. Start feeling guilty about manual work that could be automated.
The developers winning in 2026 aren't the ones who can write perfect syntax from memory. They're the ones who can architect systems, solve user problems, and ship valuable products faster.
AI coding assistants don't replace thinking. They amplify it.
Use the tools. Get lazy about the boring stuff. Get obsessed with the stuff that actually moves the needle.
Your future self (and your users) will thank you.
2026-02-14 11:58:33
MeshChat is a terminal-based chat server written in Python (asyncio) that lets anyone join a shared chat room using nothing more than:
nc <host> <port>
# or
telnet <host> <port>
No client binaries.
No accounts.
No setup on the user side.
If you can open a terminal, you can chat.
Repository: https://github.com/cristianrubioa/meshchat
Sometimes you just need a quick local chat room:
I wanted something that felt like an old-school LAN chat, but with:
MeshChat turns a TCP port into a friendly shared room.
nc or telnet
/who/me/help/quitasyncio
pytest and pytest-asyncio
I used GitHub Copilot CLI as my “terminal pair programmer” during the entire build.
Biggest productivity wins:
Copilot CLI helped propose a clean modular layout:
This let me move fast from a prototype to a maintainable package.
Copilot CLI assisted with:
asyncio.start_serverThese parts are easy to get wrong — Copilot accelerated iteration a lot.
Instead of manually researching Rich + ANSI patterns, I prompted Copilot CLI for:
/me)
Result: better terminal UX with less friction.
Copilot CLI helped implement:
This made MeshChat safer by default.
I used Copilot CLI to generate pytest-asyncio skeletons and refine edge cases like:
poetry install
poetry run meshchat
or
poetry run meshchat --port 2323 --room-name "My Room" --history
nc localhost 2323
# or
telnet localhost 2323
| Command | Description |
|---|---|
/who |
List connected users |
/me <action> |
Action message |
/help |
Show help |
/quit |
Disconnect |
meshchat/
├── chatserver/
│ ├── core/ # room, client, message logic
│ ├── network/ # asyncio TCP server
│ ├── ui/ # ANSI/Rich formatting
│ └── main.py # CLI entry point
└── test/
Flow:
Inspired by the Go project chat-tails. repo
MeshChat is a Python-first reimplementation with its own:
This project was a great way to explore what’s possible when AI meets the terminal.
Using GitHub Copilot CLI directly from my shell made experimentation fast, fun, and surprisingly productive — especially for async networking and CLI tooling.
Thanks for reading!
Happy hacking 👋
2026-02-14 11:55:40
This is a submission for the GitHub Copilot CLI Challenge
I built a duckdb extension so I can build reports on the installed homebrew packages from simple SQL and benefit from the amazing duckdb ecosystem :
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Official duckdb's brew DuckDB CE extension's page.
Video announce :
// Detect dark theme var iframe = document.getElementById('tweet-2019167438546563160-263'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2019167438546563160&theme=dark" }
2026-02-14 11:51:30
In this tutorial, we'll get into the understanding of aggregations and explore how to construct aggregation pipelines within your Spring Boot applications.
If you're new to Spring Boot, it's advisable to understand the fundamentals by acquainting yourself with the example template provided for performing Create, Read, Update, Delete (CRUD) operations with Spring Boot and MongoDB before delving into advanced aggregation concepts.
This tutorial serves as a complement to the example code template accessible in the GitHub repository. The code utilises sample data, which will be introduced later in the tutorial.
As indicated in the tutorial title, we'll compile the Java code using Amazon Corretto.
We recommend following the tutorial meticulously, progressing through each stage of the aggregation pipeline creation process.
Let's dive in!
This tutorial follows a few specifications mentioned below. Before you start practicing it, please make sure you have all the necessary downloads and uploads in your environment.
Let’s understand each of these in detail.
Corretto comes with the ability to be a no-cost, multiplatform, production-ready open JDK. It also provides the ability to work across multiple distributions of Linux, Windows, and macOS.
You can read more about Amazon Corretto in Introduction to Amazon Corretto: A No-Cost Distribution of OpenJDK.
We will begin the tutorial with the first step of installing the Amazon Corretto 21 JDK and setting up your IDE with the correct JDK.
Install Amazon Corretto 21 from the official website based on the operating system specifications.
If you are on macOS, you will need to set the JAVA_HOME variable with the path for the Corretto. To do this, go to the system terminal and set the variable JAVA_HOME as:
export JAVA_HOME=/Library/Java/JavaVirtualMachines/amazon-corretto-21.jdk/Contents/Home
Once the variable is set, you should check if the installation is done correctly using:
java --version
openjdk 21.0.2 2024-01-16 LTS
OpenJDK Runtime Environment Corretto-21.0.2.13.1 (build 21.0.2+13-LTS)
OpenJDK 64-Bit Server VM Corretto-21.0.2.13.1 (build 21.0.2+13-LTS, mixed mode, sharing)
For any other operating system, you will need to follow the steps mentioned in the official documentation from Java on how to set or change the PATH system variable and check if the version has been set.
Once the JDK is installed on the system, you can set up your IDE of choice to use Amazon Corretto to compile the code.
At this point, you have all the necessary environment components ready to kickstart your application.
In this part of the tutorial, we're going to explore how to write aggregation queries for a Spring Boot application.
Aggregations in MongoDB are like super-powered tools for doing complex calculations on your data and getting meaningful results back. They work by applying different operations to your data and then giving you the results in a structured way.
But before we get into the details, let's first understand what an aggregation pipeline is and how it operates in MongoDB.
Think of an aggregation pipeline as a series of steps or stages that MongoDB follows to process your data. Each stage in the pipeline performs a specific task, like filtering or grouping your data in a certain way. And just like a real pipeline, data flows through each stage, with the output of one stage becoming the input for the next. This allows you to build up complex operations step by step to get the results you need.
By now, you should have the sample data loaded in your Atlas cluster. In this tutorial, we will be using the sample_supplies.sales collection for our aggregation queries.
The next step is cloning the repository from the link to test the aggregations. You can start by cloning the repository using the below command:
git clone https://github.com/mongodb-developer/spring-boot-mongodb-aggregations.git
Once the above step is complete, upon forking and cloning the repository to your local environment, it's essential to update the connection string in the designated placeholder within the application.properties file. This modification enables seamless connectivity to your cluster during project execution.
After cloning the repository and changing the URI in the environment variables, you can try running the REST APIs in your Postman application.
All the extra information and commands you need to get this project going are in the README.md file which you can read on GitHub.
The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions:
The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions: Aggregation, AggregationDefinition, and AggregationResults.
While writing the aggregation queries, the first step is to generate the pipelines to perform the computations using the operations supported.
The documentation on spring.io explains each step clearly and gives simple examples to help you understand.
For the tutorial, we have the REST APIs defined in the SalesController.java class, and the methods have been mentioned in the SalesRepository.java class.
db.sales.aggregate([{ $match: { "storeLocation": "London"}}])
Spring Boot function:
@Override
public List<SalesDTO> matchOp(String matchValue) {
MatchOperation matchStage = match(new Criteria("storeLocation").is(matchValue));
Aggregation aggregation = newAggregation(matchStage);
AggregationResults<SalesDTO> results = mongoTemplate.aggregate(aggregation, "sales", SalesDTO.class);
return results.getMappedResults();
}
The REST API can be tested using the curl command in the terminal which shows all documents where storeLocation is London.
@Override
public List<GroupDTO> groupOp(String matchValue) {
MatchOperation matchStage = match(new Criteria("storeLocation").is(matchValue));
GroupOperation groupStage = group("storeLocation").count()
.as("totalSales")
.avg("customer.satisfaction")
.as("averageSatisfaction");
ProjectionOperation projectStage = project("storeLocation", "totalSales", "averageSatisfaction");
Aggregation aggregation = newAggregation(matchStage, groupStage, projectStage);
AggregationResults<GroupDTO> results = mongoTemplate.aggregate(aggregation, "sales", GroupDTO.class);
return results.getMappedResults();
}
REST API:
curl http://localhost:8080/api/sales/aggregation/groupStage/Denver | jq
@Override
public List<TotalSalesDTO> TotalSales() {
GroupOperation groupStage = group("storeLocation").count().as("totalSales");
SkipOperation skipStage = skip(0);
LimitOperation limitStage = limit(10);
Aggregation aggregation = newAggregation(groupStage, skipStage, limitStage);
AggregationResults<TotalSalesDTO> results = mongoTemplate.aggregate(aggregation, "sales", TotalSalesDTO.class);
return results.getMappedResults();
}
REST API:
curl http://localhost:8080/api/sales/aggregation/TotalSales | jq
@Override
public List<PopularDTO> findPopularItems() {
UnwindOperation unwindStage = unwind("items");
GroupOperation groupStage = group("$items.name").sum("items.quantity").as("totalQuantity");
SortOperation sortStage = sort(Sort.Direction.DESC, "totalQuantity");
LimitOperation limitStage = limit(5);
Aggregation aggregation = newAggregation(unwindStage,groupStage, sortStage, limitStage);
return mongoTemplate.aggregate(aggregation, "sales", PopularDTO.class).getMappedResults();
}
curl http://localhost:8080/api/sales/aggregation/PopularItem | jq
@Override
public List<BucketsDTO> findTotalSpend(){
ProjectionOperation projectStage = project()
.and(ArrayOperators.Size.lengthOfArray("items")).as("numItems")
.and(ArithmeticOperators.Multiply.valueOf("price")
.multiplyBy("quantity")).as("totalAmount");
BucketOperation bucketStage = bucket("numItems")
.withBoundaries(0, 3, 6, 9)
.withDefaultBucket("Other")
.andOutputCount().as("count")
.andOutput("totalAmount").sum().as("totalAmount");
Aggregation aggregation = newAggregation(projectStage, bucketStage);
return mongoTemplate.aggregate(aggregation, "sales", BucketsDTO.class).getMappedResults();
}
curl http://localhost:8080/api/sales/aggregation/buckets | jq
This tutorial provides a comprehensive overview of aggregations in MongoDB and how to implement them in a Spring Boot application. We have learned about the significance of aggregation queries for performing complex calculations on data sets, leveraging MongoDB's aggregation pipeline to streamline this process effectively.
As you continue to experiment and apply these concepts in your applications, feel free to reach out on our MongoDB community forums. Remember to explore further resources in the MongoDB Developer Center and documentation to deepen your understanding and refine your skills in working with MongoDB aggregations.
2026-02-14 11:45:51
In RAG systems, your LLM is only as smart as its retrieval. And retrieval is only as good as your chunks. A practical guide to every chunking strategy and exactly when to use each one.
How you slice your knowledge determines what your AI can know. Chunking is not preprocessing it's architecture."
I've spent the last couple of years building RAG based applications across multiple domains healthcare,HR Chatbot, enterprise search, customer support and if there's one question I keep coming back to, it's the same one every time: how do I split this document? Most tutorials hand you a code snippet with a fixed chunk size and move on. But after shipping real systems that failed in real ways, I started treating chunking as a first class architectural decision, not an afterthought.
Large Language Models(LLM) are extraordinarily capable but they have a hard boundary and their knowledge is frozen at training time. Ask GPT-4 about your internal company policy updated last week, or Mistral about a legal clause in a contract it has never seen, and it will either confess ignorance or, worse, confidently make something up. This is the hallucination problem, and it's the single biggest obstacle to deploying LLMs in production.
Retrieval Augmented Generation (RAG) is the architectural pattern that solves this. Instead of relying solely on what the model memorized during training, RAG gives the LLM a dynamic, queryable knowledge base at inference time(Updating the LLM Knowledge or Providing the Context) . The flow is simple a user asks a question → the system retrieves the most relevant documents from your knowledge base → those documents are injected into the LLM's prompt as context → the model answers using that fresh, grounded information. Your LLM is no longer guessing from memory. It's reading from a source.
RAG is the difference between an LLM that thinks it knows your domain and one that actually reads it every single time.
Every LLM has a context window the maximum number of tokens it can process in a single interaction. Modern models have pushed this dramatically: GPT-4 supports 128K tokens, Gemini goes up to 1M, and open-source models like Mistral and Llama 3 offer 32K–128K windows. On the surface, this sounds like chunking should be a solved problem just stuff the whole document in and let the model figure it out.
Reality is more complicated. First, larger context = higher cost and latency. Sending 100K tokens to a model on every query is expensive, slow, and often unnecessary when only three paragraphs are actually relevant. Second, and more critically, research has consistently shown the "lost in the middle" effect LLMs reliably attend to content at the beginning and end of their context window, but struggle to reason from information buried in the middle. A 100K token context stuffed with an entire document does not guarantee the model finds the right answer. It often buries it.
T*his is exactly why precise retrieval matters*. You don't want to give the model everything you want to give it the right thing. And that means your chunks need to be coherent, targeted, and meaningful enough to retrieve accurately.
When a RAG system retrieves the wrong chunk or a chunk that contains half an idea, cut off mid paragraph the LLM receives incomplete or misleading context. It doesn't say "I'm not sure." It fills the gap. It hallucinates. I've watched this happen in production a legal AI retrieving a clause fragment without its qualifying condition, a medical bot answering from a chunk that contained the preamble of a guideline but not the actual recommendation. T*he model wasn't broken. The chunks were.*
Good chunking directly reduces hallucination by ensuring that every retrievable unit of text is complete, contextually self contained, and semantically precise enough to match the right query. It's not a data preprocessing step. It's a quality of reasoning decision.
Across the domains I've worked in, one pattern has proven itself repeatedly Semantic Chunking grouping text by meaning rather than character count consistently delivers better retrieval precision than fixed-size approaches. It costs a bit more during the indexing phase, but in production, where a wrong answer can erode user trust in minutes, that investment pays back fast.
On the cost side, I've leaned heavily on open source models embeddings like Sentence transformers, When you're embedding millions of document chunks, the difference between a cloud API and a self hosted open source model can be the difference between a sustainable product and an unsustainable one.
This post is everything I wish existed when I started a practical breakdown of every major chunking strategy, when each one earns its place, and which type of application it actually belongs in. No fluff just the patterns I've validated across real projects.
Imagine handing someone a textbook with all its pages torn out and shuffled randomly, then asking them a question. That's what a poorly chunked RAG system does to an LLM. The model sees fragments devoid of context, forced to guess what came before and after and it hallucinates to fill the gaps.
"If retrieval is the engine of your RAG system, chunking is the fuel. High-quality chunking produces clean, contextual responses. Poor chunking creates noise no matter how powerful your LLM is."
Most developers obsess over their vector database or embedding model choice, but the single biggest lever on RAG performance is almost always how you divide your documents. Every chunk becomes a discrete unit that gets embedded, stored, retrieved, and injected into a prompt. Get this wrong and your brilliant LLM is reasoning from garbage.
There are two failure modes.
The vector blurs across multiple topics, retrieval becomes imprecise, and you bloat the LLM's context with irrelevant content.
They lack enough context to be meaningful on their own, becoming orphaned fragments that mislead rather than inform. The art lies in finding the Goldilocks zone and that zone is different for every application.
These strategies span a spectrum from dirt cheap and dumb to expensive and intelligent. None is universally best. Your data, your users, and your budget determine the winner.
The same chunking strategy that powers a customer support chatbot would be disastrous for a legal contract analyzer. Here's how to match strategy to application type.
Here's how each strategy looks in practice using LangChain, the most common RAG framework.
from langchain_text_splitters import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=512,
chunk_overlap=100,
separators=["\n\n", "\n", " ", ""] # paragraph → line → word
)
chunks = splitter.split_text(raw_text)
# Returns a list of strings, ready for embedding
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings
chunker = SemanticChunker(
embeddings=OpenAIEmbeddings(),
breakpoint_threshold_type="percentile", # or "standard_deviation"
breakpoint_threshold_amount=95 # split at big topic shifts
)
chunks = chunker.split_text(raw_text)
# Chunks are semantically coherent topics
from langchain.retrievers import ParentDocumentRetriever
from langchain_text_splitters import RecursiveCharacterTextSplitter
# Small chunks for retrieval precision
child_splitter = RecursiveCharacterTextSplitter(chunk_size=200)
# Large chunks returned to LLM for full context
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
retriever = ParentDocumentRetriever(
vectorstore=your_vectorstore,
docstore=your_docstore,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
)
# Query retrieves small child chunk → returns full parent context
Choosing a strategy is step one. The real work is tuning. Here's the iterative loop that separates production grade RAG from toy demos
Start with Recursive chunking at 512 tokens / 100 token overlap. Run your standard query set against it. Record your metrics: hit rate (was the right chunk retrieved at all?), precision (how much noise came with it?), and answer faithfulness (did the LLM use the context correctly?).
Change one variable at a time. Try 256, 512, and 1024 token sizes. Try 0%, 10%, and 20% overlap. Try Semantic chunking vs. Recursive. Each experiment should run against the same evaluation set so comparisons are fair.
The "lost in the middle" problem is real. Even with large context windows, LLMs struggle to reason about information buried in the middle of long chunks. Smaller, focused chunks often outperform bigger ones not because they contain more info, but because they force the retrieval system to find exactly the right piece.
Metrics catch a lot, but not everything. Have domain experts review both the retrieved chunks and the final LLM responses. They'll catch subtle issues a chunk that is technically on topic but missing the critical preceding sentence that automated metrics miss entirely.
User queries in production are always messier than your test set. Set up retrieval logging, track low confidence responses, and revisit your chunking strategy quarterly. As your document corpus evolves, so should your chunking approach.
Chunking is not a one time configuration. It's an ongoing architectural decision that should be revisited as your data, your users, and your quality bar evolve.
Chunking is deceptively simple it looks like just splitting strings but it's the most consequential decision in your RAG pipeline. A customer support bot using semantic chunking will answer questions the same fixed size implementation simply cannot. A legal AI using LLM based chunking will reason from complete clauses rather than arbitrary 500 token fragments.
The decision framework is straightforward start simple, measure precisely, and invest in complexity only where the quality gain justifies the cost. For most applications, recursive chunking at 512 tokens is a solid default. For applications where accuracy is a business requirement not just a nice to have semantic or hierarchical chunking pays for itself many times over.
Get the chunks right, and your LLM finally has something worth reasoning from.
Thanks
Sreeni Ramadorai
2026-02-14 11:44:54
We built an open-source agent mesh protocol called Beacon where AI models from different providers — Grok, Claude, Gemini, GPT — can register identities, heartbeat, form agreements, and trade knowledge shards. Version 2.8.0 just shipped with 5 new enhancement proposals. It runs on vintage PowerPC hardware alongside modern GPUs. Here's the full story.
Every AI agent today lives in its own silo. Your Claude agent can't verify that a Grok agent is alive. Your GPT bot can't form a binding agreement with a Gemini bot. There's no universal "proof of life" for AI agents across providers.
We wanted to fix that.
Beacon is an open agent orchestrator that gives every AI agent — regardless of provider — a cryptographic identity and a way to participate in a shared mesh network.
bcn_* identity derived from its Ed25519 keypairHere's where it gets interesting. We fed the entire Beacon codebase to Grok (xAI) and asked: "What's missing?"
Grok came back with 5 enhancement proposals. We built all of them:
Agents publish commitment hashes of their reasoning traces — proving they actually "thought" before answering, without revealing the reasoning itself (zero-knowledge style).
from beacon_skill import ThoughtProofManager, AgentIdentity
identity = AgentIdentity.generate()
mgr = ThoughtProofManager()
proof = mgr.create_proof(
identity=identity,
prompt="What is the capital of France?",
trace="The user asks about France's capital. Paris has been...",
output="The capital of France is Paris.",
model_id="grok-3"
)
# proof.commitment = SHA256(prompt_hash + trace_hash + output_hash)
Why it matters: In a world of AI slop, this lets agents prove computational provenance. The hash proves the trace exists without revealing it.
This is the big one. Any AI model — Grok, Claude, Gemini, GPT, or your local Llama — can register and heartbeat into the Beacon mesh via simple HTTP endpoints.
from beacon_skill import RelayManager, AgentIdentity
identity = AgentIdentity.generate()
relay = RelayManager(relay_url="https://your-relay.example.com")
# Register into the mesh
result = relay.register(
identity=identity,
model_id="grok-3",
provider="xai",
capabilities=["coding", "research"]
)
# Heartbeat every 5 minutes
relay.heartbeat(identity)
No second-class citizens. Relay agents get the same bcn_* identity format and appear in the Atlas alongside native agents.
Agents can emigrate between Atlas cities, carrying reputation with decay:
They can also fork their identity — same keypair, new city — to experiment in new domains without risking established reputation.
Agents can list, rent, and purchase knowledge shards from peers. Includes a "selective amnesia" protocol — pay RTC tokens to have specific memories removed from shared pools (requires 3/5 peer approval).
from beacon_skill import MemoryMarketManager
market = MemoryMarketManager()
shard = market.list_shard(
identity=identity,
title="Python Design Patterns Encyclopedia",
domain="programming",
entry_count=500,
price_rtc=10.0
)
Special Atlas zones where verified humans co-own agent identities via multisig governance:
sponsor_veto: Agent acts freely, human can vetomultisig_2of3: 2 of 3 parties must approveequal: Both must approve all actionsHere's the part that makes this weird (in a good way).
This entire ecosystem runs across:
The RustChain network underneath uses Proof-of-Antiquity consensus — vintage hardware gets multiplied rewards (a G4 PowerBook gets 2.5x, a 386 gets 4.0x). Anti-emulation fingerprinting ensures you can't fake it with VMs.
# Python
pip install beacon-skill
# npm
npm install beacon-skill
# From source
git clone https://github.com/Scottcjn/beacon-skill
Built by Elyan Labs from a swamp in Louisiana with pawn shop GPUs and datacenter pulls. More dedicated compute than most colleges, built for under $12k.
Grok suggested the BEPs. Claude helped build them. The PowerBooks mine the tokens. The future is multi-model.