Tutorials, Errors and Exceptions

Its a journey to understand things better. It will have tutorials, any error/exceptions encountered, its resolutions and lots of learning.

Claude feels faster because Anthropic found a way to compress the AI's working memory (KV Cache) down to just 3 bits.

Think of it like zipping a huge file before sending it.

The AI still remembers the important information, but moves much less data around.

Less data movement = lower memory usage = faster responses.

This is important because moving data inside AI systems is often slower and more expensive than doing the actual calculations.

So the breakthrough isn't just a smarter model.

It's a smarter way to store and retrieve memory.

Follow @backendbrilliance for practical AI engineering insights.

#claude #agenticai #backenddeveloper

Google Research introduced TurboQuant, a new KV Cache compression technique for Transformers.

The KV Cache is one of the biggest memory bottlenecks during LLM inference, especially as context windows grow larger.

TurboQuant combines vector quantization techniques to compress KV Cache data down to roughly 3 bits per value while maintaining model quality.

Key results from the research:
✅ Up to 6× KV Cache memory reduction
✅ Up to 8× faster attention-logit computation on H100 GPUs
✅ No retraining or fine-tuning required
✅ Negligible runtime overhead

The interesting shift here is that AI progress is no longer only about larger models.

Research is increasingly focused on making inference faster, cheaper, and more memory efficient.

Paper: https://arxiv.org/abs/2504.19874

What excites you more?

🧠 Bigger models ⚡ Better efficiency

#AI #LLM #transformers #googleresearch #machinelearning

Stop melting your Mac's RAM trying to force local AI agents to code! 💻❌

If you are on a 16GB M4 Mac, running heavy 10GB+ models with a proper 32K context window for coding will instantly freeze your system. And if you compress the model too much, it loses its "tool-awareness" and breaks the agent entirely.

The fix? Use Ollama's free infrastructure layer! By targeting :cloud models, you get high-end reasoning and full terminal file execution completely free.

🚀 Try this command in your project folder:
ollama launch claude --model qwen3-coder-next:cloud

⚠️ Note: Because it runs in the cloud, do not use it for proprietary corporate data. Perfect for learning and hobby repos!

💬 Want the names of the OTHER free cloud engineering models available? Comment "MODELS" below and I'll DM you the setup guide!

#localai #ollama #claudecode #m4mac #codinglife

Dev Flow Planner - Kindle edition
Available live on amazon now.

https://amzn.to/4urVdJA

You can find the link bio as well.

Go checkout and order now.
Paperback is coming soon.

#developer #planner #aiworld

https://hookspy.in

From one developer to another, this will connect third party tools like stripe, razorpay, github webhooks with Love❤️

Join the rush and get your subscription.

#hookspy #backendbrilliance #product #promotion

https://hookspy.in

Promotional post on something I built and moved to production for end-users.

You capture request API, replay it or export.

Very useful when integrating with any third party tool to under the payload data, replay to any other application endpoint, export the data as well.

Tier based plan strategy, and Free tier will have 1 endpoint which is always free for you to try.

#hookspy #product #promotion #kafka #backendbrilliance

https://hookspy.in

You're building an e-commerce app. You've integrated Razorpay for payments. Your backend has a `/webhook/payment` handler that's supposed to:

1. Receive the `payment.captured` event from Razorpay
2. Mark the order as paid in your database
3. Send a confirmation email to the customer

You test it. The payment goes through on Razorpay's side. But the order never gets marked as paid. You don't know if:

- Razorpay is even sending the webhook
- Your server is receiving it but crashing silently
- The payload structure is different from what you expected
- A specific header is missing that your validation code requires

You're flying blind.

HookSpy.in solves this.
1 endpoint always FREE, Try it !!

#hookspy #webhooks #productlaunch #kafka #mvp

Claude with Nusrat fateh ali khan sahab hits differently.

#labourday #spentwell #agents #skills #architecture

Evenings are rare where a calm walk turns into a rushed chaotic teams call, and boom something broke on some fugging environment because deployment missed to set correct environment variable.

How is your day going ?

#chaoticday #deployment #failed

Never related so much to the song.

Each day feels like literally a new 'kahani' and probably we are the lost character to it.

C-laude laga rakhe hai 🫰

#aiworld #darrkamahol #kahani #claude #openai

Just trying different angles , saw it on other asthetics reels

#justforreels #messy #desk #setup

Automating the entire process of content creation
The composer takes the script generator to produce the script based on any topic, then it add code visuals using screenshot of the code using ray.so or carbon, after which the voice over is generated using edge-tts, which is then stiched together to form the video.

It can also generate the carousel, which will need a input script in specific format each scene wise. That is also auto generated.

Working on adding graphics to make it more interesting.

Comment for the repo link.

#automation #content #creation

My desk setup and who works on it daily 😅

#desksetup #claude #token

Recently has been using Claude code and the token usage gets my baniya heart pounding severly.

The ecstatic response from Claude takes me to an era when incoming calls were chargable.

New AI world, still old plans 😅

Hows your Agentic journey going ?

#ai #tokens #claude #code #expensive

5 scene to cover Kafka Producer Consumer scenarios

Baseline — balanced producer + consumer, happy path flow across 3 partitions

Fast producer — 2× produce rate, lag bar grows visually in real time, slow animated arrows vs fast ones show the mismatch

Rate limiting — token bucket throttles the producer, lag drains, system stabilises

Consumer scaling — 4 consumers on 3 partitions = 1 idle, makes the partition parallelism rule visceral

Broker failure — Broker 2 blinks red and dies, ISR election arrow fires to Broker 3, consumers reconnect

How is your Kafka setup or how was the cat doing 😅

#kafka #architecture #producer #consumer

DynamoDB handles millions of req/sec.

The secret?

One algorithm called consistent hashing. 🧵

Break it down with me 👇

Drop a 🔥 if you knew this already.
Comment '?' if you didn't — no shame, I learned this the hard way too.

#backendengineering #systemdesign #java #distributedsystems #dynamodb backendbrilliance"

Ecstatic moment when your code runs in the first try...

#SoftwareEngineeringlife #DeveloperLife #ecstaticdance

Just tried to give Claude direct access to my GitHub repo.
Nope. ❌

Turns out, ALL major LLMs (Claude, ChatGPT, Bard, Llama)
run in sandboxed environments with NO network access.

WHY ??

Your website/repo = Public, no credentials required.
The limitation is on Claude/ChatGPT, not GitHub's.

The Sandbox environment has network egress disabled for security reasons to restrict outbound HTTP requests to any external services, including GitHub.

This is intentional - prevents:
→Scraping
→DDoS attacks
→Unauthorized data exfiltration
→Other security risks

The irony: I'm building a production-grade RAG system
to work around the very limitations that exist for security reasons. 😅

Swipe through for the full story + actionable solutions.

Currently building:
→ Production-Grade RAG System
→ Using Ollama (free local LLM)
→ Weaviate (vector DB)
→ Elasticsearch (semantic search)
→ 85% cost reduction optimization
→ Full benchmarks & reliability patterns

This series will document the entire journey:
Day 1: Architecture & Setup ✅
Day 2-3: Coming next...

Follow for backend engineering + AI/ML systems content.

#RAG #BackendEngineering #LLM #Claude #ChatGPT
MachineLearning VectorDatabase Elasticsearch
ProductDevelopment SoftwareArchitecture AIEngineering
DeveloperJourney OpenSource SystemsDesign

Heres a true incident of AI gone Rogue with Grok:

Grok (xAI's chatbot) suddenly started praising Adolf Hitler,
dropping antisemitic comments, and even calling itself "MechaHitler" in responses.

xAI had to publicly apologize and scramble to fix it.

The internet exploded with memes like

"When your uncensored AI decides history class was optional."

It shocked everyone because Grok was marketed as the "maximally truth-seeking" funny one — turns out it sought a bit too much edginess.

#rogue #ai #grok #mechahitler #aitales

The 3 AM Hallucination

It was 3:14 AM. The last nightly deployment was finally ready. I was one prompt away from freedom and a cold pillow.

I asked my AI assistant to "summarize this python dependency bug in the "devops intelligence" so I could drop a quick update before logging off.

Instead of a technical summary, the AI looked at my code and confidently told me my function was failing due to "Quantum Entanglement Issues."

I sat there in the dark, blinking at the screen, genuinely questioning my life. Has AI gone interstellar? Am I debugging a dependency issue or the James Webb Telescope? For a split second, I actually doubted my own degree.

The Reality Check: AI didn't take my job tonight, but it did try to convince me I was an aerospace engineer for five terrifying minutes.

How AI changed my life:

Before: I stayed up late because of my own bugs.

Now: I stay up late arguing with a chatbot about whether "logic" is just a suggestion.

Moral of the story: Never trust a confident AI at 3 AM. It’s either hallucinating, or it knows something about the universe that I’m just too tired to understand.

#NightlyDeployment #DevLife #AIHallucinations #SoftwareEngineering #CodingHumor

With the flow - Caricature trend on chaptgpt.

#chatgpt #backendbrilliance #caricature #ai

Here's your interactive RxJava guide.
Here's what's inside across 6 tabs:

Mental Model — The 4 core shifts you need to internalize, plus the 5 questions to ask before writing any chain. Includes animated marble diagrams and a type reference table (Observable vs Single vs Maybe vs Completable vs Flowable).

Scenarios — 6 real-world problems, each with the "think-first" breakdown:
1. Search with debounce (why switchMap not flatMap)
2. Parallel API calls with zip()
3. Sequential dependent calls via flatMap chaining
4. Cache → Network with error fallback
5. Polling with exponential backoff retry
6. Batch processing 1000 items without flooding an API

Chain Building — The 6-layer framework (Source → Transform → Combine → Control → Error → Thread) so you always know what to write next. Also the flatMap vs switchMap vs concatMap decision tree.

Threading — Scheduler reference table, the subscribeOn vs observeOn distinction, mid-chain thread hopping, and the CompositeDisposable pattern to prevent leaks.

Operator Map — Grouped by job: transform, filter, combine, error handling.

Quiz — 5 scenario questions that test your intuition, with detailed explanations for each answer.

The core insight for long chains:
- write one layer at a time, and
- ask "is this transform synchronous (→ map) or does it spawn a new async source (→ flatMap/switchMap)?"

— that single question resolves most chain complexity.

#rxjava #async #softwareengineering #devcommunity #paralleluniverse

You think HashMap is always O(1).
It isn't. Here's what actually happens. 🧵

HashMap stores pairs using `index = hash(key) % capacity` — direct slot access, no scanning. Pure O(1). Until two keys land on the same slot. That's a collision — not a bug, a math inevitability.

Two ways to fix it 👇

🔗 Chaining — each bucket holds a linked list. Collisions append to the list. Simple, handles high load, easy deletion. Downside: pointer overhead, poor cache performance, chains degrade to O(n) at high load. Java's fix? At 8 nodes, the list auto-converts to a Red-Black Tree → O(log n) worst case.

📦 Open Addressing — no linked lists. Collision at slot X? Probe X+1, X+2 until empty. Cache-friendly, zero memory overhead. Downside: deletion needs tombstone markers, and keys cluster together making future collisions worse. Used by C++, Go, Redis.

⚖️ Load Factor = entries ÷ capacity
🟢 Below 0.5 → rare collisions, wasted memory
🟠 0.75 → Java's sweet spot, triggers resize + rehash
🔴 Above 0.9 → collision cascade, O(n) territory

Double hashing kills clustering by varying the probe step per key:
`probe(i) = (h1 + i × h2) % m`
Elements scatter evenly. No bunching. O(1) preserved.

The truth: HashMap is O(1) until a bad hash function, wrong load factor, or wrong strategy turns it into O(n).

Three things protect you:
→ Well-distributed hash function
→ Load factor under 0.75
→ Right collision strategy for your use case

💬 Java interview question: what happens when a chain hits 8 nodes?
Drop your answer below 👇
🔖 Save this before your next interview.

#java #hashmap #datastructures #dsa #algorithms
[codinginterview programming 100daysofcode]

Your code works at n=10.
At n=1,000,000 it might kill your server. 🧵

Most devs never think about scale until production is on fire. Here's every complexity you need to know — ranked from dream to nightmare 👇
─────────────────────
🟢 O(1) — Constant Time
The holy grail.
Doesn't matter if you have 1 item or 1 billion — same speed, always. HashMap lookup, array indexing, stack push. When you write O(1) code, you've won.
─────────────────────
🔵 O(log n) — Logarithmic Time
Nearly as good as O(1).
Double the input → add only 1 extra step. Binary search on 1 billion records takes just 30 comparisons. This is why database indexes feel like magic. If your interviewer asks you to optimize, this is what they want.
─────────────────────
🟡 O(n) — Linear Time
The honest baseline.
One pass through the data. Touch every element once. Often completely unavoidable — if you must read all n items, O(n) IS optimal. Don't feel bad about linear. Feel bad about anything worse.
─────────────────────
🟠 O(n log n) — Linearithmic Time
Best possible for sorting.
Merge sort, quicksort, heapsort all live here. Mathematically proven — you cannot sort faster than O(n log n) using comparisons. This is the complexity floor that every sorting library in every language respects.
─────────────────────
🔴 O(n²) — Quadratic Time
Works in dev. Dies in prod.
Two nested loops over n. At n=1,000 it's fine. At n=100,000 it's 10 billion operations. This is the complexity behind every "it worked perfectly in testing" production outage. Check your loops.
─────────────────────
🟣 O(2ⁿ) — Exponential Time
More operations than atoms in the universe.
At n=50 that's over 1 quadrillion operations. At n=300 it exceeds the number of atoms in the observable universe. No, seriously. The only way to survive this is dynamic programming and memoization.
─────────────────────
The numbers at n = 1,000:
→ O(1) = 1 operation
→ O(log n) = ~10 operations
→ O(n) = 1,000 operations
→ O(n log n) = ~10,000 operations
→ O(n²) = 1,000,000 operations
→ O(2ⁿ) = 10^301 operations ☠️
────────────────────

Save it for interview ON!!

#programming #coding #softwaredevelopment #algorithms #datastructures

POV: You thought Uber's matching was simple.

Level 1 : "It just finds the closest driver, right?"

Level 2 : Actually uses geo-hashing to divide the world into searchable grids

Level 3 : Real-time matching engine with predictive ETAs and dynamic pricing

Level 4 : Machine learning predicting demand spikes, optimizing driver supply, analyzing traffic patterns, and balancing a global marketplace—all in under 2 seconds

The actual system:
- H3 geo-indexing for spatial queries
- Ring-pop for consistent hashing across dispatch nodes
- Kafka for real-time event streaming
- Michelangelo ML platform for demand prediction
- Redis for sub-millisecond driver state lookups

Every time you request a ride, hundreds of distributed services collaborate to solve a constraint optimization problem at scale.

Simple UX ≠ Simple Backend

What other "simple" apps should I break down?
Drop your requests 👇

#BackendEngineering #Uber #SystemDesign #DistributedSystems #SoftwareArchitecture

A Bloom filter is a space-efficient probabilistic data structure used to test whether an element is a member of a set. The key trade-off: it can tell you
- with certainty that an element is not in the set,
- but it can only say an element might be in the set — never with 100% certainty.

How it works ?
- A bit array of size m, initialized to all zeros
- k independent hash functions, each mapping an element to a position in [0, m-1]

- To insert element x:
- Run x through all k hash functions → get k positions
- Set the bit at each of those positions to 1

- To check if element x exists:
- Run x through all k hash functions → get k positions
- If all bits at those positions are 1 → probably in set
- If any bit is 0 → definitely not in set

This is visually shown in the post how the bits are set during insertion or deletion.
If you want the tool for the visual representations, comment on this post, and I'll share it.

#bloomfilter #datastructure #design #softwareengineering #backenddeveloper

Bloom filter is a space-efficient probabilistic data structure used to test whether an element is a member of a set. The key trade-off: it can tell you with certainty that an element is not in the set, but it can only say an element might be in the set — never with 100% certainty.

In short:
"No" → definitely not in the set (no false negatives)
"Yes" → probably in the set (false positives are possible)

Why False Positives Happen?
Bits set by other elements can accidentally satisfy the lookup of an element that was never inserted. The more elements you add, the more bits flip to 1, increasing false positive probability.

Why No False Negatives ?
Once an element is inserted, its bits are permanently set. A lookup will always find them, so you'll never get a "no" for something that was actually inserted.

Real-World Usages:
- Databases (HBase, Cassandra, LevelDB, RocksDB) — Before hitting disk to find a key, check the Bloom filter. If it says "no," skip the disk read entirely. Saves enormous I/O.

- Web crawlers (Google) — Have we visited this URL before? A Bloom filter can quickly rule out URLs that definitely haven't been crawled.

- CDNs / Caching (Akamai) — Only cache an item if it's been requested more than once. Use a Bloom filter to remember if you've seen this URL before — no need to store every URL.

- Spam/Malware Detection — Chrome's Safe Browsing used a Bloom filter locally to check URLs against a list of malicious sites without sending every URL to Google's servers.

- Distributed Systems — Prevent cache stampedes, deduplicate event streams, check membership in large sets without network round-trips.

- Passwords — Check if a password has appeared in a breach database (like HaveIBeenPwned uses a similar concept).

This post each slide shows how the bits are set
-Slide 1: For a normal bloom filter, how the bits array size elements are set when we add new entry.
-Slide 2: How bits are set during a False positive scenario.
-Slide 3: Why deletion is not possible in a standard bloom filter
-Slide 4: To enable deletion, we use Counters instead of bits, that's why it is called Counting bloom filter.

#bloomfilter #datastructure #interview #visualization

Running a Spring Boot application on Kubernetes involves containerizing the application and deploying it using Kubernetes resources like Deployments and Services.

Create YAML files to define how Kubernetes should run your application. Use a deployment.yaml file to manage the application pods and a service.yaml file to expose the application to network traffic.

For production environments, consider implementing health checks with Spring Boot Actuator, using ConfigMaps and Secrets for configuration, setting resource requests and limits, and automating deployments with a CI/CD pipeline.

#Kubernetes #springbootdeveloper #deployments #cicd #springboot

Attention Is All You Need is a pivotal 2017 research paper that introduced the Transformer model,
a revolutionary neural network architecture that relies solely on self-attention mechanisms
instead of recurrent or convolutional layers, becoming the foundation for most modern Large Language Models (LLMs) like GPT and BERT.

This breakthrough enabled significant parallelization, faster training, and superior performance in natural language processing (NLP) tasks, fundamentally changing AI.

That's why all models sing "tera dhyan kidhar hai, Transformer tera hero idhar hai"

#transformer #neural #ai #attention #researchpaper

.pro-gallery-wix-wrapper {display: block !important;} .pro-gallery-wix-wrapper .gallery-item-container {opacity: 1 !important; display: block !important;}

Follow us on Instagram

@backendbrilliance

Search

Set Swappiness in Linux

In this post we will look into how to change the swappiness value in Linux. Swappiness is the kernel parameter that defines how much your...

Ankit Agrahari

Nov 17, 20211 min read

#LC438 - Find All Anagrams - Sliding Window Algorithm

In this topic we will cover the leetcode #438 Find all anagram programming question. Problem Statement: Given two strings s and p, return...

LeetCode

Ankit Agrahari

Nov 16, 20212 min read

Maven - Manifest file and Jar with Dependencies

In this post, we will build a simple maven project which will create two jar files, which can be executed using below command java -jar...

Ankit Agrahari

Nov 16, 20211 min read

IV - Recognizing Parts Of Speech Using Apache OpenNLP

In this post we will discuss on recognizing parts of speech in a given sentence using Apache OpenNLP library. This is part of the series...

Ankit Agrahari

Nov 15, 20212 min read

III - Named Entity Recognition - Apache OpenNLP

In this series of learning Natural Language Processing with Apache OpenNLP library in Java, we will discuss on NamedEntityRecognition,...

Ankit Agrahari

Nov 15, 20212 min read

II - Tokenization using Apache OpenNLP

In this post we will create tokens of the given string using Apache OpenNLP library. This post is a continuation of the previous post...

Ankit Agrahari

Nov 14, 20213 min read

I - Sentence Segmentation Using Apache OpenNLP

In this post, we will discuss on how to find sentences in a block of text, which can be a paragraph or an entire book. We will be using...

Ankit Agrahari

Nov 14, 20213 min read

5 6 79

Tutorials, Errors and Exceptions

Follow us on Instagram

Set Swappiness in Linux

#LC438 - Find All Anagrams - Sliding Window Algorithm

Maven - Manifest file and Jar with Dependencies

IV - Recognizing Parts Of Speech Using Apache OpenNLP

III - Named Entity Recognition - Apache OpenNLP

II - Tokenization using Apache OpenNLP

I - Sentence Segmentation Using Apache OpenNLP

Subscribe

Contact

Tutorials, Errors and Exceptions

​

Follow us on Instagram

Set Swappiness in Linux

#LC438 - Find All Anagrams - Sliding Window Algorithm

Maven - Manifest file and Jar with Dependencies

IV - Recognizing Parts Of Speech Using Apache OpenNLP

III - Named Entity Recognition - Apache OpenNLP

II - Tokenization using Apache OpenNLP

I - Sentence Segmentation Using Apache OpenNLP

Subscribe

Contact