DEV Community: Nik L.

The Rise of On-Device AI and the Return of Data Ownership

Nik L. — Tue, 22 Jul 2025 10:34:24 +0000

1. Why we left the cloud

For years, developers turned to the cloud because they had to, not because it was the best option. The cloud made intelligent software possible, but it came with trade-offs: privacy concerns, latency, cost unpredictability, and a total reliance on someone else’s infrastructure.

At Pieces, we decided to try something different. We rebuilt our AI stack from the ground up to run entirely on the user’s device.

No cloud calls. No API gateways. No hidden compute bills.

The goal wasn’t just “offline mode.” It was about taking back ownership of performance, of privacy, and of control.

2. A faster, leaner, smarter AI stack

Instead of using a single giant language model to do everything, we broke the problem into smaller parts.

We built a collection of small, focused models, each trained for a specific task:

Is this request about the past or the future?
Should it summarize, remind, or plan?
What data span does it reference?

Each model is compact, with between 20M and 80M parameters, and is built to run fast, locally, and predictably. They hand off results to one another like a microservice chain, passing structured outputs through a smart pipeline.

The result?

Inference times under 150ms
No token-based costs
Up to 16% better accuracy than GPT-4, Gemini Flash, and LLaMA-3 3B on our benchmarks
55× higher throughput

3. It’s not just us, this is an industry shift

We’re not alone in this thinking. The industry is pivoting.

From Apple’s A18 chip to Microsoft’s Copilot+ PCs and Snapdragon’s latest NPUs, hardware is being optimized for local AI execution. Even Chromebooks now come with tensor accelerators built-in.

Hugging Face CEO Clément Delangue summed it up best:

“Everyone’s asking for more data centers, but why aren’t we talking more about running AI on your own device?”

When developers realize that local AI isn’t a limitation but a better foundation, the shift becomes obvious.

4. How on-device AI actually works?

On-device AI means the model runs where you are—on your laptop, phone, or edge device.

There’s no cloud request. No latency spike. No invisible pipeline.
Inference happens entirely in memory, with your data staying private and under your control.

At Pieces, we built our entire system around this. Using Ollama, you can run full LLMs offline on an Apple Silicon Mac or any supported Windows GPU. You can even switch between cloud and local models mid-conversation, without losing chat history or breaking context.

It’s built for anyone working without reliable internet—or working with data that simply can’t leave the machine.

5. Privacy isn’t a feature, it’s the architecture

Most AI products treat privacy like a setting you can toggle. But true data ownership means your data never leaves the device in the first place.

No outbound calls. No token logs. No inference history sitting on someone else’s server.

On-device AI flips the entire risk model. It shrinks the attack surface to your own machine, where you can audit what runs and how it behaves.

That’s why it’s resonating with teams in finance, healthcare, legal, and other regulated industries. This isn’t just about passing GDPR or CCPA—it’s about building infrastructure that never leaks in the first place.

6. Cloud costs vs. fixed compute

Running large models in the cloud isn’t just expensive—it’s unpredictable. You’re charged per token, per user, per request, often without a clear way to budget or forecast usage.

With on-device AI, you pay once (via compute) and inference is free after that. The cost model becomes architectural, not transactional. And your stack scales linearly, not exponentially.

The energy savings are huge, too. Consider this:

Model Size	Setup	CO₂ per 1M Tokens
77M (local CPU)	Your laptop	0.14g
70B (cloud GPU)	20× A100s	400–530g
GPT-4-class (MoE)	H100 cluster	900–1300g

That's over 1000× more energy to get the same answer in the cloud. When multiplied across millions of users, the difference becomes systemic.

7. Where on-device AI shines

Not every AI task needs to be on-device. But many of the ones users care about the most do:

Voice transcription
Summarization
Local memory and search
Language translation
Keyboard suggestions
Code snippet enhancement

These don’t need 175B parameters. They need speed, precision, and trust—the kind that comes from running close to the user.

And with energy, latency, and privacy now being design constraints—not afterthoughts—on-device AI becomes not just viable, but inevitable.

8. So what now?

This isn’t about keeping up with the cloud. It’s about choosing a different path, one where AI is embedded, not streamed.

Where memory is local.
Where costs don’t scale with usage.
Where you know what your model is doing, and where.

The moment we started asking real questions—

“Do we need GPT-4 to detect a URL?”
“Why send calendar data to the cloud to get a reminder?”

—the answer became obvious.

We didn’t just optimize our pipeline. We changed the foundation.

And we’re building from there.

If you’re curious how Pieces works locally, let’s talk.

Article written by Tsavo, CEO, Pieces and fully accessible here

🧠 Pieces AI Memory: Built for Real Developer Workflows

Nik L. — Fri, 13 Jun 2025 04:49:46 +0000

Memory is the new frontier in AI.

From OpenAI’s persistent memory in ChatGPT, to Claude’s evolving context windows, to Copilot and Cursor tracking dev history—memory is transforming the way we build software.

At Pieces, we’ve embraced this shift from day one.

🔁 Always-On Context

Pieces AI Memory isn't just reactive. It’s a proactive layer that silently powers your dev workflow, in real-time.

By the time you take a screenshot, Pieces has already created contextual memories of what you're working on.

No prompts.
No friction.
Just seamless awareness.

🧠 Long-Term Memory—Engineered for Developers

This is true developer memory:

Stores what matters—code blocks, conversations, tasks
Works behind the scenes
Doesn’t interrupt your flow

It’s not a clipboard. It’s continuity.

🔌 Built Into Your Stack

Memory should work wherever you do. So we integrated Pieces AI Memory into a growing ecosystem of tools:

Supported Platforms Include:

Extracted Markdown Links:
GitHub Copilot
Cursor
long-term memory
Actual Budget
Acumbamail
Adobe Flash Builder
AdobeDreamweaver
Airtable
AItable
Amazon S3
Android Studio
Apify
Aptana Studio
Asana
ASocks
AWS Cloud9
Baserow
BBBedit
Bettermode
Blockscout
BlueGriffon
Brackets
Bubble
Cal.com
Calendly
Caret
Cartloom
Certopus
Chargekeep
CKEditor
Clearout
CloudShell
Cloutly
Codeanywhere
CodeLobster IDE
Codenvy
CodePen
Coder
CodeSandbox
Contiguity
ConvertKit
Copy AI
CotEditor
Cursor
Customer io
Dappier
Dillinger
Discord
DocRaptor
Doom Emacs
Dracula
Dust
Eclipse
Eclipse IoT
Ecplipse IDE
ElevenLabs
Ethereum
Feedly
Flagsmith
Fliqr AI
Flowise
Flowlu
FocusWriter
Formbricks
Frame
Freshdesk
Froala Editor
GCloub Pub/Sub
Geany
GenerateBanners
GhostCMS
Github
Github Codespaces
GitPod
Glitch
GNOME
GNU Emacs
Google Calendar
Google Contacts
Google Docs
Google Drive
Google Forms
Google Gemini
Hemingway
https://pieces.app/ai-memory/code-blocks
iA Writer
Instagram for business
Intelij IDEA
IsDown.app
Jina AI
Jopin
JSFiddle
Jypyter
Kakoune
Kate Editor
KomotoEdit
Light Table
Line Bot
Literature and Latte
LyX
MacDown
ManuSkript
Mark Text.app
Markdown by DaringFireball
MarkdownPad
Medullar
Metabase
Micro
Microsoft Azure Communications
Microsoft Azure OpenAI
Microsoft Dynamics 365
Microsoft Expression Web
Microsoft Visual Studio
Nano
Neovim
Netbeans
Node.JS
Notepad
Notepad2
Nova Code Editor
Obsidian.md
Overleaf
Pastebin.com
PhpStorm
Pinegrow
PSPad
PulpMiner
PyCharm
Qt Creator
Quill
Rentry.co
Replit
RStudio
RubyMine
SlackBlitz
Spacemacs
StackEdit
Straico
SublimeText
Summernote
Tekken 7
Telegram Bot
TeXstudio
TextMate
TextWrangler
Thonny
TinyMCE
Typora
UltraEdit
Ulysses.app
Umso
Vim
Vim-pug
Visual Studio Community
VS code
Vscode.dev
VSCodium
Webstorm
Windows Notepad
WordCounter.net
Xcode
here

🔗 See full list of integrations →

No switching tabs.
No copying and pasting.
Memory follows you.

🧩 From Assistant to Operating System

This isn't just an assistant bolted onto your tools.
This is a real-time memory layer that weaves your scattered workflows together.

That snippet you copied last week? Still there.
The conversation where you debugged an issue? Instantly recalled.
The context around your current task? Already captured.

It’s not just assistance. It’s persistence.

🚀 Built to Evolve With You

Got a tool that's not supported yet?
Is something in your workflow breaking your focus?

Tell us.
We’re not building for you—we’re building with you.

🔍 Learn More

🌐 Explore Integrations
🧠 Discover best practices and prompt strategies
💬 Talk to us — your feedback shapes this product

Pieces AI Memory is always learning, always adapting—just like you.

We Fine-Tuned our OCR to Read Code: Here’s What It Took (and What Broke)

Nik L. — Wed, 21 May 2025 04:43:15 +0000

What is Optical Character Recognition?

Optical Character Recognition (OCR) is a foundational computer vision technology that converts printed or handwritten text from images or scanned documents into machine-readable digital text. Traditional OCR systems analyze the shape, position, and pattern of characters in an image, mapping them against a pre-trained character model to extract structured text.

OCR has become critical in transforming analog documents into searchable and editable formats, driving use cases such as:

Document digitization
Automated data entry
Accessibility enhancements (e.g., text-to-speech for visually impaired users)

Recent advances, particularly in machine learning and deep neural networks, have significantly improved OCR’s accuracy across diverse domains and languages.

OCR for Code at Pieces

At Pieces, we’ve extended OCR’s capabilities beyond traditional document processing by tailoring it to recognize and accurately transcribe programming code from images. This adaptation is critical, as source code demands not only character-level accuracy but also preservation of layout and syntactic structure.

OCR Engine Choice: Tesseract + LSTM

We selected Tesseract—an open-source OCR engine—as our base. Tesseract supports over 100 languages and integrates LSTM-based sequence prediction, offering a solid starting point for structured text recognition. However, out-of-the-box, Tesseract is not optimized for code syntax or indentation.

To address this, we developed a specialized OCR pipeline with pre-processing, post-processing, and layout inference tailored to the needs of developers.

Image Pre-Processing for Code Screenshots

To optimize OCR for code, we standardized inputs through a robust image pre-processing pipeline, particularly for images captured from:

IDEs (e.g., VS Code, IntelliJ)
Terminals and command lines
Code screenshots from YouTube tutorials or blog posts

Key Challenges & Solutions

1. Dark Mode and Color Inversion

Tesseract performs best on binarized, light-background images. We implemented an automatic dark-mode detection pipeline:

Median blur to reduce visual outliers
Pixel brightness thresholding to classify image mode
Inversion applied conditionally for dark backgrounds

2. Noisy or Gradient Backgrounds

We apply a dilation + median blur technique:

A duplicate image is blurred and dilated
Subtracting the blurred image from the original removes background noise while preserving text edges

3. Low-Resolution Images

Using bicubic upsampling, we scale images to improve OCR performance. Although we evaluated SRCNN (Super-Resolution CNN) and found it comparable in accuracy, its computational overhead and storage requirements led us to favor bicubic for production use.

Post-OCR: Code Layout and Indentation Inference

OCR for code requires structure preservation—particularly indentation, which is semantically critical in languages like Python.

Layout Inference Strategy:

We leverage Tesseract’s bounding boxes per line
By computing average character width per box and comparing starting X-coordinates, we infer relative indentation
A heuristic is applied to normalize indent levels to even-space units (e.g., 2 or 4 spaces)

This enables rendering of clean, readable, and semantically valid source code from OCR output.

Evaluation Methodology

We evaluate each modification in our pipeline through empirical validation using handcrafted and synthetic datasets of code-image pairs.

Evaluation Metrics:

Levenshtein Distance: Measures edit distance between OCR output and ground truth
Hypothesis-driven testing: Each enhancement (e.g., upsampling method, noise removal) is treated as a hypothesis, validated through A/B testing across datasets

For example:

Hypothesis: SRCNN will outperform bicubic interpolation for low-res code images
Result: Bicubic delivered comparable accuracy with lower resource overhead, and was chosen for production

Summary: Tailoring OCR for Code is Non-Trivial

Standard OCR engines are not code-aware. They:

Ignore indentation
Struggle with noisy UIs
Lack syntax sensitivity

Our enhancements—preprocessing, layout-aware postprocessing, and tailored evaluation—enable production-grade OCR for developers, delivering usable, syntactically correct code from screenshots and video frames.

Get Started with Pieces OCR

You can experience our OCR model by downloading the Pieces desktop app, built for seamless code extraction from images.

We’re also expanding our developer tooling ecosystem:

Integrations MCP with GitHub and Cursor
Recent implementation of MCP workflows

Interested in our APIs? Contact: email me.

Top 10 AI Models Every Developer Should Know in 2025

Nik L. — Wed, 30 Apr 2025 12:04:58 +0000

Before diving into specific models, let's clarify the distinct categories of AI models available today:

Category	What They Do	Example Use Cases
LLMs	Process and generate human-like text	Code generation, content creation, API documentation
Generative Models	Create content across multiple modalities	Custom UI assets, wireframes, mockups
Computer Vision	Process and understand visual data	OCR integration, document analysis, UI testing
Recommendation Systems	Predict user preferences	In-app content personalization, user engagement
Time Series Models	Analyze sequential data patterns	System monitoring, anomaly detection
Reinforcement Learning	Learn through trial and error	Training agents, optimization problems
Graph Neural Networks	Process node-edge relationships	Network analysis, dependency mapping
GANs	Generate realistic synthetic data	Testing data, simulation environments
Transformer Models	Process sequences with attention	The foundation for most modern LLMs
Decision Tree Models	Make predictions through branches	Rapid classification, feature importance

How I selected the models in this list

In this article, I listed 10 AI models that you can use to build AI-powered applications.

These recommendations come from personal experience (I will also list use cases so you can better understand when to use each one), research papers, articles on models that achieved the best performance for certain tasks (like YOLO for computer vision), some lesser know Reddit threads (which often acts as a gold mine of information to me) and Peter Yang’s famous article on “An Opinionated Guide on Which AI Model to Use in 2025”.

When it comes to models, we are spoiled with choices now (Even while writing this article, I learnt about Llama 4 being launched).

I personally use 2-3 different models for different types of tasks.

My go-to ones are Claude 3.7 Sonnet for coding and 4o for creative tasks such as writing. Along with these, I also use tools like v0/Bolt to build the frontend, Pieces for help within the IDE, and acting as a second brain for me.

While I cannot cover everything AI-related in this one blog, I will list some of the best Gen AI models that you should know about and also cover how you can use them in your daily tasks.

Top 10 AI Models for Developers in 2025

1. GPT-4o: The Versatile Creator

Strengths:

Multimodal capabilities (text, image, audio)
128k token context window (16x larger than GPT-4)
Significant improvements in image understanding and generation
50% cheaper API costs than previous models

When to use it:
GPT-4o excels at creative tasks requiring imagination and flair. It's particularly strong for generating UI copy, ideation, creating documentation, and producing visual assets.

// Example: Using GPT-4o API for image generation
const response = await openai.images.generate({
  model: "gpt-4o",
  prompt: "Create a Ghibli-style UI dashboard for a plant monitoring application",
  n: 1,
  size: "1024x1024",
});

Developer tip: While powerful for creative tasks, GPT-4o can struggle with complex, multi-step code challenges. It's best paired with a more code-focused model for development workflows.

2. Claude 3.7 Sonnet: The Developer's Companion

Strengths:

Exceptional at code generation and debugging
Strong reasoning capabilities with extended thinking mode
Excellent at extracting information from complex diagrams and technical docs
Better at following technical instructions precisely

When to use it:
Claude 3.7 Sonnet shines when you need accurate, well-structured code, especially for front-end projects. It's particularly valuable for understanding and generating code from diagrams, screenshots, and technical documentation.

# Example: Using Claude API for code generation from a diagram
import anthropic

client = anthropic.Client(api_key="your_key")
response = client.messages.create(
    model="claude-3-7-sonnet-20250219",
    max_tokens=4000,
    system="You're an expert React developer.",
    messages=[
        {"role": "user", "content": [
            {"type": "text", "text": "Convert this wireframe into React code with Tailwind CSS"},
            {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "..."}}
        ]}
    ]
)

Developer tip: Claude's "extended thinking" mode (available to Pro users) significantly improves its performance on complex reasoning tasks, making it worth the investment for intricate development problems.

3. YOLO (You Only Look Once): Computer Vision Workhorse

Strengths:

Single-pass architecture for real-time object detection
Works on resource-constrained devices (mobile, Raspberry Pi)
Supports multiple tasks beyond object detection
Easy integration via multiple libraries/formats

When to use it:
YOLO is indispensable when your app needs to "see" the world - whether for real-time object detection, gesture recognition, or augmented reality applications.

# YOLOv8 implementation for real-time object detection
from ultralytics import YOLO

# Load the model
model = YOLO('yolov8n.pt')

# Run inference on an image
results = model('path/to/image.jpg')

# Process results
for r in results:
    boxes = r.boxes
    for box in boxes:
        x1, y1, x2, y2 = box.xyxy[0]
        confidence = box.conf[0]
        class_id = box.cls[0]

Developer tip: YOLOv8 can be exported to ONNX, TFLite, and other formats for cross-platform deployment, making it ideal for edge device implementations.

4. BERT: The NLP Foundation Model

Strengths:

Bidirectional understanding of context
Pre-trained versions available for specific domains
Excellent at classification and semantic understanding
Lightweight compared to modern LLMs

When to use it:
BERT remains a go-to choice for specialized text classification, sentiment analysis, named entity recognition, and information extraction tasks where full LLMs would be overkill.

# Using BERT for sentiment analysis with Hugging Face
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")

# Analyze sentiment
text = "Your app has transformed my development workflow!"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

# Get sentiment (1-5 stars)
sentiment = torch.argmax(logits, dim=1).item() + 1

Developer tip: For search functionality, BERT-based embeddings can provide better semantic understanding than traditional keyword approaches at a fraction of the cost of newer embedding models.

5. LLaMA: Open Source Foundation

Strengths:

Fully open-source
Runs locally for privacy and reduced latency
Available in multiple sizes (7B to 70B parameters)
Strong foundation for fine-tuning custom models

When to use it:
LLaMA models are ideal for applications requiring local inference, privacy guarantees, or customized behavior through fine-tuning.

# Running LLaMA locally with Ollama
import ollama

# Generate text with LLaMA
response = ollama.generate(
    model='llama:4',
    prompt='Explain how to implement JWT authentication in a Node.js app',
    options={'temperature': 0.7}
)

print(response['response'])

Developer tip: The latest LLaMA 4 models offer multimodal capabilities similar to cloud services but with the privacy benefits of local execution, making them worth considering for sensitive applications.

6. Whisper: Audio Intelligence

Strengths:

Exceptional multilingual speech recognition
Handles noisy audio gracefully
Works offline with optimized implementations
No fine-tuning needed for most use cases

When to use it:
Whisper is the go-to model for any application requiring speech-to-text capabilities, from podcast transcription to voice commands and meeting notes.

# Transcribing audio with Whisper
import whisper

model = whisper.load_model("base")  # Options: tiny, base, small, medium, large

# Transcribe file
result = model.transcribe("audio.mp3")

# Get transcription text
print(result["text"])

Developer tip: For real-time applications, consider using whisper.cpp, which offers significantly faster performance on CPU-only environments while maintaining quality.

7. XGBoost: The ML Reliable

Strengths:

Exceptional performance on structured data
Built-in cross-validation
Interpretable feature importance
Low resource requirements

When to use it:
XGBoost remains the first choice for predictive analytics on tabular data, from user behavior prediction to fraud detection and recommendation systems.

# Basic XGBoost implementation for classification
import xgboost as xgb
from sklearn.model_selection import train_test_split

# Prepare data
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)

# Train model
model = xgb.XGBClassifier(
    n_estimators=100,
    learning_rate=0.1,
    max_depth=5
)
model.fit(X_train, y_train)

# Get feature importance
importance = model.feature_importances_

Developer tip: XGBoost's feature importance metrics provide valuable insights for product development, helping identify which user behaviors or attributes most strongly predict outcomes.

8. Stable Diffusion: Visual Creation Engine

Strengths:

Generates high-quality images from text prompts
Runs locally on consumer GPUs
Highly customizable through fine-tuning
Extensive ecosystem of extensions

When to use it:
Stable Diffusion is perfect for generating design assets, mockups, illustrations, and visual content for applications and websites.

# Using Stable Diffusion with Hugging Face
from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5")
pipe = pipe.to("cuda")

# Generate image
prompt = "A minimalist mobile app UI for plant care tracking, isometric view"
image = pipe(prompt).images[0]
image.save("app-mockup.png")

Developer tip: The latest Stable Diffusion 3.5 version introduces significant quality improvements for technical illustrations and UI mockups, making it particularly useful for developers visualizing concepts.

9. Mistral 7B: The Efficient Performer

Strengths:

Exceptional performance-to-size ratio
Low latency inference
Works well on limited hardware
Open weights for customization

When to use it:
Mistral 7B is ideal for production applications requiring responsive AI capabilities without enterprise-level infrastructure, such as in-app assistants and real-time generators.

# Using Mistral 7B with Hugging Face
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")

# Generate response
input_text = "Explain how to implement pagination in a REST API"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0])

Developer tip: Mistral's efficient architecture makes it an excellent choice for edge deployments where you need LLM capabilities closer to the user.

10. Granite 3.0: Enterprise-Ready AI

Strengths:

Developer-friendly license (Apache 2.0)
Strong multilingual support
Trained on 100+ programming languages
Reduced bias and toxicity

When to use it:
Granite 3.0 is perfect for building enterprise applications requiring clear licensing terms and robust safety guardrails.

# Using IBM's Granite 3.0 with Hugging Face
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "IBM/granite-3b-code-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = """
Write a Node.js function to securely store user passwords using bcrypt.
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Developer tip: Granite's strong focus on programming languages makes it particularly valuable for code generation tasks in enterprise environments where licensing concerns are paramount.

Making the Right Choice: A Developer's Decision Framework

When selecting an AI model for your project, consider this decision framework:

Task specificity: Is this a specialized task (like object detection) or a general task (like text generation)?
Infrastructure constraints: Local deployment or cloud-based? CPU or GPU?
Latency requirements: Is real-time performance critical?
Licensing needs: Open source or proprietary? Commercial use constraints?
Customization level: Off-the-shelf or fine-tuned to your domain?

Conclusion: Build Your AI Stack Strategically

The most effective developers in 2025 aren't using a single AI model for everything - they're strategically combining specialized models into a cohesive AI stack:

GPT-4o for creative tasks and initial prototyping
Claude 3.7 Sonnet for precise code generation and technical documentation
YOLO for computer vision components
Whisper for voice interfaces
Domain-specific models (like BERT variants) for specialized functions

By understanding the strengths and limitations of each model, you can leverage AI as a true force multiplier in your development workflow, focusing your human creativity and problem-solving skills where they matter most.

Remember, the goal isn't to know every model out there - it's to build an intuition for which tool fits which job, and to stay curious about emerging capabilities that could transform your development process.

Nano-Models for Temporal AI - We created this new breakthrough to offload temporal understanding entirely to local hardware

Nik L. — Wed, 23 Apr 2025 16:22:38 +0000

⚡ Nano-Models for Temporal AI: Pieces’ LTM-2.5 Breakthrough

Latency. Privacy. Cost. Until recently, you had to choose two.

When you're dealing with long-term memory for intelligent systems, especially at the OS level, there’s a painful truth: just identifying when to look can cost more compute (and user trust) than finding the info itself.

Most pipelines offload that problem to cloud LLMs — parsing user intent, generating time spans, normalizing input, scoring relevance, etc. That adds seconds of latency, cloud costs that scale with token volume, and worst of all, exposes highly personal context in transit.

🧠 The Breakthrough: LTM-2.5

We recently dropped a breakthrough: two nano-models, trained via distillation, quantized, pruned, and optimized to run directly on consumer hardware.

The first model figures out if a query involves time, and if so, what kind: “What was I working on just now?” vs. “What am I doing tomorrow?”
The second model extracts the exact time span(s) implied by user language. Think “just before lunch yesterday” or “sometime last summer.”

Together, they replace a 10–15 step cloud pipeline, reducing latency to milliseconds, keeping all data on-device, and removing reliance on remote inference altogether.

🛠️ Why It Works

Intent classifier: >99% accuracy, real-time inference on consumer CPUs
Span predictor: high IoU & coverage even for fuzzy or implied queries
Runs completely offline — zero token cost, zero cloud dependency

No orchestration, no round trips, no privacy compromises.

🔍 What It Unlocks

Point-in-time recall: “What was I just doing?”
Temporal search: “Show me last week around Friday”
Scheduling vs. retrieval differentiation
Smart timeline navigation without scanning the full corpus

And that’s just for temporal memory. This is one of 11 nano-models inside LTM-2.5 — all working toward intelligent, privacy-first memory at the OS layer.

We open-sourced some of the architecture and benchmarks — check it all out in the full breakdown here →

👉 Read the full deep dive

🔥 Introducing Pieces MCP Server: Your AI Tools Just Got a Memory Upgrade of 9 Months Context Window

Nik L. — Fri, 18 Apr 2025 13:40:00 +0000

Imagine your AI tools could remember what you did yesterday. Not just what files you opened, but why you changed that dependency, what Judson said in your meeting, or what was discussed in your late-night Slack brainstorm. Now they can.

Say hello to the Pieces MCP Server.

It's live, it's open, and it's making your AI tools smarter by plugging them into your actual work history.

Memory for Your Favorite Dev Tools

The Pieces MCP Server connects Pieces Long-Term Memory (LTM) to any MCP-compatible client—like GitHub Copilot, Cursor, and more. That means your coding copilot now has context. Real, useful, personalized memory.

Try this prompt:

"Based on yesterday’s convo with Laurin, update my package manifest to use the latest versions."

The MCP client talks to the Pieces MCP server, grabs the memory, and updates your code using its built-in agent. No tab-switching. No digging. Just instant recall.

⚙️ What It Actually Does (TL;DR)

If you’ve been wondering “What’s this MCP buzz about?”,this explainer has you covered. But here’s the skinny:

The Pieces MCP Server integrates directly into your dev tools.
It delivers contextual memory to your LLM of choice (Copilot, Cursor, etc.).
You keep using the AI tools you already love—but now with memory superpowers.

🔧 How to Get Started

You can be up and running in minutes:

Update to the latest version of Pieces.
Copy your local MCP server URL from the Pieces menu bar.
Paste it into your MCP client (e.g., Copilot, Cursor).
Ask time-aware or source-specific questions.

Boom. You’re good.

👀 Want help? Grab our setup guides:

Prefer a visual walkthrough? Watch the videos here.

🛠️ Why It’s Built Different

We went with SSE (Server-Sent Events) for communication. It’s fast, lightweight, and already plays nice with PiecesOS—unlike those clunky stdio setups that need Node or extra baggage.

✅ Works out of the box with:

GitHub Copilot
Cursor
Goose
Cline
Windsurf

❌ Not supported (yet): Claude Desktop (but there’s a workaround using lightconetech/mcp-gateway).

🧪 What Can You Ask?

Get specific. Get powerful. Try:

“What was I working on yesterday?”
“Refactor utils.py using yesterday’s PR feedback.”
“Summarize Judson’s meeting notes and update the README.”
“Implement the GitHub issue I was just looking at.”

If your client supports tool-calling, it’ll auto-decide when to hit up Pieces. Want to be direct? Just say:

“Ask Pieces to…”

🕵️ Under the Hood (For the Curious)

Here’s the data flow when you ask a question:

Your MCP client passes your prompt to its LLM.
LLM figures out it needs context → calls ask_pieces_ltm.
The client hits the Pieces MCP Server.
Pieces sends back relevant memories.
Your client’s LLM builds a reply using that context.

Pieces itself doesn’t process or modify anything. It’s modular. It’s secure. And it just works.

🔍 Feature Highlights

💡 Tool-Agnostic: Use it from any MCP-compatible client.
🕰️ Time-aware & Source-aware: Ask what you did in VS Code last Tuesday.
🤖 Agent Ready: Let LLMs apply memory-based changes directly in your code.

💸 Token Costs & Tips

Heads-up: Using Pieces MCP adds some token overhead.

Tool descriptions get included in the initial prompt.
Memory responses add a second pass.

No worries though—just disable Pieces when you’re not using it, and fire it back up when you need that brain boost.

🚀 Ready to Try It?

Just open up your MCP client of choice, ask something with context, and watch the magic happen.

Got a cool workflow? Show us what you're building:
PiecesForDev on X, LinkedIn, Bluesky, or Discord.

I Tested 50+ LLMs and The Results Were Surprising.

Nik L. — Fri, 04 Apr 2025 13:38:22 +0000

Large Language Models (LLMs) are everywhere now – GPT-4, Claude 3, Gemini, LLaMA, Mistral, and more. Everyone talks about which is "the best," but surprisingly, real side-by-side performance comparisons are rare. So, I built one myself.

I tested over 50 LLMs – both cloud-based and local – on my own hardware, using real-world developer tasks. And the results? Shocking.

Microsoft's Phi-4 was the most accurate model overall (yes, a local model!).
IBM’s Granite models outperformed many of OpenAI’s most hyped offerings.
Speed vs. accuracy is a serious tradeoff – and the best choice depends on your workflow.

Here's a breakdown of how I tested, what I found, and how you can pick the right model.

🛠️ Testing Setup

I used the Pieces C# SDK to build a test harness that could consistently run prompts across cloud and local models. Each test was repeated five times, and I averaged the results based on:

Time to first token
Time to complete response
Output accuracy (measured against expected results)

My Hardware

M3 MacBook Air (24GB RAM)
Tested models with up to 15B parameters (anything larger couldn't run on-device)
All cloud models supported by Pieces Copilot were included

👉 Want more details on the testing setup? Check out my long-form article on the Pieces blog.

📌 Test Scenarios

I didn’t just throw synthetic benchmarks at these models – I used actual developer tasks, simulating real-world usage. Where applicable, tasks leveraged Pieces' Long-Term Memory (LTM) for better context.

Tasks included:

🗂 Converting JSON into Markdown tables
✉️ Summarizing email chains
🛠 Answering GitHub issues & NuGet docs
📝 Suggesting code fixes in VS Code
🔎 Extracting insights from Reddit threads

⚡ Fastest Models

⏳ Fastest to First Token (Cloud)

🥇 Claude 3 Opus – 2.2s

🥈 Gemini 2.0 Flash – 2.4s

🥉 Gemini 1.5 Flash – 2.5s

Even the slowest cloud model (GPT-4 Chat) was only 0.9s behind Claude 3 Opus. Cloud models are clearly optimized for speed.

🚀 Fastest Local Model

🥇 Code Gemma 1.1 7B – 7s to first token

😬 Accuracy? Just 5%.

🎯 Most Accurate Models

This was unexpected.

🥇 Phi-4 (Microsoft, Local) – 82% accuracy

🥈 GPT-4o (OpenAI, Cloud) – 78% accuracy

🥉 Granite 3.1 Dense 8B (IBM, Local) – 78% accuracy

Mind-blowing: The top-performing model doesn't need a cloud API or premium pricing – it's free, downloadable, and runs locally (if your hardware can handle it). Also, IBM’s Granite models beat Claude and Gemini in multiple tasks.

🏆 Fastest to Full Response

🥇 Gemini 1.5 Flash – 1.6s

🥈 Gemini 2.0 Flash – 1.7s

🥉 PaLM2 (deprecated) – 1.9s

For local models, Granite 3 MOE 1B was the fastest (4.5s), though accuracy was just 13%. Meanwhile, Phi-4 – the most accurate model – took 2+ minutes to generate responses. That’s the tradeoff.

🤔 Why Do LLMs Perform So Differently?

Even with the same input and context, LLMs return wildly different results. Why?

System Prompts Matter – Some models need different prompt engineering (e.g., reasoning vs. conversational models).
Context Window Limits – A 4K token model can't process as much as a 128K token model.
Training Data & Architecture – Code-tuned models (e.g., Qwen Coder) behave differently from general LLMs.
Hardware Constraints – Bigger local models hit bottlenecks on lower-end devices, forcing CPU fallback = slower output.
Parameter Count – More parameters ≠ better, but generally lead to deeper reasoning.

🏅 Overall Winner: GPT-4o (OpenAI)

Scoring System

50–1 points per metric (accuracy, first token, full response)
Accuracy weighted 2x more

🥇 GPT-4o took the crown – not the fastest, but the most balanced.

🥈 GPT-4o Mini & PaLM2 followed closely.

Biggest surprise? Google deprecated PaLM2 in October 2024, yet it still outperformed newer models. 🤷‍♂️

🔍 So… What Should You Use?

There’s no one-size-fits-all LLM. But here’s a cheat sheet:

Need	Model Recommendation
Accuracy + Local Execution	🏆 Phi-4 (if your hardware can handle it)
Speed + Good-enough Results	⚡ Gemini 1.5 Flash / Claude 3 Opus
Balanced Performance	🎯 GPT-4o Mini

My Personal Picks

Local: Granite 3.1 Dense 8B – accurate, more practical than Phi-4
Cloud: GPT-4o Mini – fast, reliable, accurate

This content was written by Jim Bennett, head of Devrel at Pieces for Developers. You can find more interesting visualization images of the analysis like below here - https://pieces.app/blog/best-llm-models

4 Fun Techniques to Master Prompt Engineering 🎯

Nik L. — Mon, 13 Jan 2025 14:17:14 +0000

Prompt engineering is like mixing a perfect cocktail—get the right ingredients in the right amounts, and voilà, you get an amazing result! Want your AI model to serve top-tier answers? Let’s shake things up with these effective, easy-to-digest techniques.

🍸 What’s Prompt Engineering Anyway?

In a nutshell, prompt engineering is crafting specific instructions to get spot-on responses from large language models (LLMs). The more crystal-clear your prompt, the better the output.

Boring Prompt:

"Summarize prompt engineering."

Cool Prompt:

"Give a 100-word summary of prompt engineering for non-techies."

💡 Result: You now have a concise, user-friendly explanation. Nice!

🎯 Key Ingredients for a Perfect Prompt:

1. Context = Clarity!

Think of context as giving your AI a head start. It narrows down the scope.

Example:

Want to modify a C# class? Instead of tossing vague requests, serve some code along.

   public class User { 
       public int UserId { get; set; }
       public string Name { get; set; }
   }

Prompt:

"Make UserId and Name read-only, set them via the constructor."

Output:

   public User(int userId, string name) {
       UserId = userId;
       Name = name;
   }

2. Be Super-Specific

General prompts = meh responses. Specific prompts = magic.

Ask for exactly what you want!

Bad: "Create a user class."

Good: "Create a C# user class with fields UserId, Name, and Email. Make UserId read-only."

3. Guide the Output

Want the AI to produce data or follow a particular style? Show a sample!

Example:

"Generate 3 users with fields UserId, Name, and Email."

Output:

   var user1 = new User(1, "Alice", "alice@example.com");
   var user2 = new User(2, "Bob", "bob@example.com");

You can try all of these prompts directly with your LLM of choice for free and see how each LLM differs in its outputs using PiecesOS.

🛠️ Techniques to Boost Your Prompt Game:

1. Zero-shot Prompting 🕶️ – No Clues, Just a Direct Ask

Think of this as asking your AI assistant to solve a problem without giving it any prior examples or context—it’s going in “cold.” Zero-shot prompting works well for straightforward tasks where the AI can infer what you want based on its training data.

Example:

"Create a unit test for the User class using xUnit in C#."

Output:

public class UserTests {
    [Fact]
    public void UserConstructor_SetsProperties() {
        var user = new User(1, "John Doe", "john@example.com", "555-555-5555");
        Assert.Equal(1, user.UserId);
        Assert.Equal("John Doe", user.Name);
        Assert.Equal("john@example.com", user.Email);
        Assert.Equal("555-555-5555", user.PhoneNumber);
    }
}

💡 When to use:

Zero-shot prompting works best for tasks where the AI can easily guess your needs, such as generating boilerplate code, creating summaries, or performing simple tasks.

2. Few-shot Prompting 🎯 – Show, Don’t Just Tell

Few-shot prompting is like teaching by example—you provide a few instances of what you want, and the AI picks up the pattern. This is particularly useful when the output requires a specific structure or format.

Example:

"Here are two instances of a User class. Now generate two more."

Prompt:

// Example instances provided by you
var user1 = new User(1, "Alice", "alice@example.com", "555-555-5555");
var user2 = new User(2, "Bob", "bob@example.com", "555-555-5556");

// Task: Generate two more instances

Output:

var user3 = new User(3, "Charlie", "charlie@example.com", "555-555-5557");
var user4 = new User(4, "Diana", "diana@example.com", "555-555-5558");

💡 When to use:

Few-shot prompting is ideal when:

The output needs to follow a specific format.
You want consistent style across different responses.
You're working with data that has structured patterns.

Pro Tip: Keep your examples short and clear. Too many examples can overwhelm the AI, while too few may not establish the pattern clearly.

3. Prompt Chaining 🔗 – Divide and Conquer

Prompt chaining is like breaking down a big problem into smaller tasks, solving each one step-by-step. This technique is particularly helpful for complex problems or multi-step workflows.

How it works:

Start simple: Begin with a basic prompt and get an initial response.
Iterate: Use follow-up prompts to refine the output or guide the model towards a more complex solution.

Example:

Goal: Create a fully-featured User class in C# with private properties and a constructor.

Step 1: Start simple.

Prompt:

"Create a basic User class in Python."

Output:

   class User:
       def __init__(self, username, email):
           self.username = username
           self.email = email

Step 2: Refine the output.

Prompt:

"Convert this Python class to C#."

Output:

   public class User {
       public string Username { get; set; }
       public string Email { get; set; }
   }

Step 3: Add additional requirements. Prompt: "Make Username and Email read-only, and add a CreatedAt property initialized in the constructor." Output:

   public class User {
       public string Username { get; }
       public string Email { get; }
       public DateTime CreatedAt { get; }

       public User(string username, string email) {
           Username = username;
           Email = email;
           CreatedAt = DateTime.Now;
       }
   }

💡 When to use:

Use prompt chaining when:

The task is too large to handle in a single prompt.
You want to progressively refine the output.
You’re iterating on a solution by adding new requirements at each step.

4. Chain-of-Thought Prompting 🧠 – Help AI Think Like a Developer

This technique involves explicitly guiding the AI through the steps it should take to solve a problem. It’s like walking the AI through your thought process, ensuring it doesn’t skip any important details.

Example:

"Write unit tests for a User class, considering key scenarios: constructor validation, edge cases, and valid phone numbers."

Prompt:

"Create unit tests for the following class.

Think step-by-step:

Identify key scenarios to test.
Write unit tests using xUnit.
Consider edge cases."

Output:

public class UserTests {
    [Fact]
    public void Constructor_ShouldInitializeProperties() {
        var user = new User(1, "John Doe", "john@example.com");
        Assert.Equal(1, user.UserId);
        Assert.Equal("John Doe", user.Name);
        Assert.Equal("john@example.com", user.Email);
    }

    [Theory]
    [InlineData("555-555-5555")]
    [InlineData("123-456-7890")]
    public void ShouldAccept_ValidPhoneNumbers(string phoneNumber) {
        var user = new User(2, "Jane Doe", phoneNumber);
        Assert.Equal(phoneNumber, user.PhoneNumber);
    }

    [Fact]
    public void ShouldThrowException_WhenPhoneNumberIsNull() {
        Assert.Throws<ArgumentNullException>(() => new User(3, "Invalid User", null));
    }
}

💡 When to use:

Chain-of-thought prompting is perfect when:

The task requires logical, multi-step reasoning.
You need the AI to think critically and not skip key details.
You're working on tasks that benefit from explicit step-by-step guidance (e.g., writing complex code or solving mathematical problems).

You can try all of these prompts directly with your LLM of choice for free and see how each LLM differs in its outputs using PiecesOS.

The article was originally written by Jim, head of Devrel at Pieces for Developers. You can find more examples and nuances in this article https://pieces.app/blog/llm-prompt-engineering

5 Fun Copilot Prompts You Can Use Today 🚀

Nik L. — Fri, 10 Jan 2025 14:59:10 +0000

As a developer, you probably have your fair share of productivity hacks—custom shortcuts, aliases, and maybe even some arcane Vim key bindings. But have you ever had a tool blow your mind with what it can do? That’s exactly how I feel about Pieces Copilot.

So, I thought it’d be cool to write a dev-friendly guide featuring 5 prompts that make devs go “Wait, Pieces can do THAT?” If you’re like me and love tools that help you get stuff done faster while avoiding distractions, buckle up—this post is for you.

1. “What’s the issue I need to look at?” (a.k.a. Anti-Distraction Mode)

Let’s be honest: staying focused as a dev is hard. Between Slack pings, email notifications, and random distractions, it’s easy to lose track of what you were supposed to do. Ever found yourself scrolling through chat threads only to realize that you still haven’t found the message you needed?

Here’s where Pieces Copilot’s Long-Term Memory becomes your new best friend. Instead of diving into Slack and risking distraction, just ask:

Prompt:

What’s the issue I need to look at?

Pieces will scan your interactions from GitHub Issues, Jira, or whatever ticketing system you use, and boom—there’s your answer without ever leaving your IDE. No Slack rabbit holes. No empty coffee cups. Just the info you need, right when you need it.

2. “How can I implement this issue in this project?” (Smart Context-Aware Guidance)

You’ve got the issue, you’re in your IDE, but… where do you start? Especially if you’re working on a new codebase, navigating it can feel like being dropped in the middle of a forest with no map.

Luckily, Pieces Copilot uses project context to guide you. Here’s what you do:

Prompt:

How can I implement this issue in this project?

If Pieces knows your project structure, it’ll give you a detailed answer—everything from routing to database setup, right down to UI components and navigation. It’s like having a senior dev by your side (without the judgmental sighs).

3. “What was the documentation I was reading?” (Goodbye, Tab Hell)

Ever had 37 tabs open and needed to find that one doc you were reading an hour ago? We’ve all been there.

With Pieces Long-Term Memory, you can just ask:

Prompt:

What was the documentation I was reading about connecting SQLite in Python?

Pieces will fetch the link and drop it into your chat. No more digging through history. No more tab roulette. Just instant access to what you need.

4. “Translate this code into Python” (Cross-Language Wizardry)

Working in a polyglot team means you’ll often get code samples in a language you don’t use. Maybe your colleague hands you some C# code, but your project’s in Python. No worries—let Pieces do the heavy lifting.

Prompt:

Translate this code into Python

Whether it’s a regex validation snippet or a complex function, Pieces will give you a clean Python version. In our SciFi store project, I got a C# regex snippet for email validation, and with one prompt, Pieces handed me a Python version that I could drop straight into my code.

5. “How can I fix this code?” (Your New Rubber Duck)

Bugs happen. Sometimes you can spot the issue in seconds; other times, you stare at your screen for hours, only to end up questioning your life choices. Instead of waiting for inspiration (or better weather for a walk), try this:

Prompt:

How can I fix this code?

In one case, I wrote some truly terrible SQLite code for our SciFi store app. It didn’t load stock properly, and I couldn’t figure out why. I asked Pieces, and it immediately pointed out that I was trying to access the row by name instead of by index. Fixed it in seconds. Thanks, AI rubber duck.

Conclusion: Hack Your Dev Workflow with Pieces Prompts

Pieces Copilot is like having a personal assistant that’s always ready to help with context-aware prompts. Whether you’re looking up issues, navigating new codebases, translating code, or rubber-ducking bugs, it’s a game-changer for dev productivity.

Got a favorite prompt or tip for using Pieces? Comment below.

Happy coding! 😎

This cool article was originally written by Jim, head of Devrel at Pieces for Developers. You can find the full article here.

You can find Jim on LinkedIn.

Claude 3.5 Sonnet vs. GPT-4o

Nik L. — Wed, 08 Jan 2025 14:18:18 +0000

In this case study, I’ll explore a detailed comparison between these two AI models, based on their performance, pricing, and specific use cases, drawing insights from community feedback, benchmarks, and personal experience.

Claude 3.5 Sonnet: Intelligent and Human-like

What is Claude?

Claude is an AI assistant developed by Anthropic, with an emphasis on ethical and human-like interactions. It’s powered by a large language model, and its development was influenced by former OpenAI members. Claude’s “Constitutional AI” approach aims to provide AI that is more aligned with human values.

Claude’s Key Features:

Claude 3.5 Sonnet is considered the most intelligent in the Claude 3.5 family, excelling in logical reasoning and handling creative tasks.
The model is designed for tasks such as summarization, research, writing, and decision-making.
Claude 3.5 is free for use with limited features, but users can upgrade to paid plans for extended functionality.

Usage Insights:
Claude 3.5 Sonnet shines in areas requiring human-like interactions and creative solutions. For instance, in personal tests, it generated highly creative and non-generic responses to prompts.

However, it lags slightly in specialized areas such as mathematical problem-solving and complex reasoning, where it shows lower accuracy than GPT-4o.

GPT-4o: Omni-Capable and Fast

What is GPT-4o?

GPT-4o is OpenAI’s latest AI model, offering a versatile approach to processing various types of input—text, audio, image, and video. The "o" in GPT-4o stands for "omni," underscoring its multimodal capabilities. This model is trained to handle complex tasks, from advanced reasoning to problem-solving across diverse domains.

GPT-4o’s Key Features:

GPT-4o excels in providing fast and accurate responses across different media types, including audio and video.
It supports complex problem-solving in fields like math, science, and coding, making it ideal for tasks that require deep analytical thinking.
It is available through OpenAI’s ChatGPT subscription service at $20/month, with API access priced at $2.50 per million tokens.

Usage Insights:
For complex tasks, GPT-4o’s performance outshines many competitors. In benchmarks, GPT-4o scored higher in areas like mathematical problem-solving, reasoning, and speed. It’s particularly useful for users requiring fast responses and multi-input-output capabilities.

Benchmarking the Models: Key Comparisons

1. Graduate-Level Reasoning (GPQA, Diamond Benchmark):

The GPQA benchmark evaluates AI's ability to handle graduate-level reasoning.

Claude 3.5 Sonnet: 59.4% accuracy on zero-shot CoT tasks.
GPT-4o: 53.6% accuracy on zero-shot CoT tasks.

Conclusion: Claude 3.5 Sonnet excels in graduate-level reasoning.

2. Math Problem-Solving (MATH Benchmark):

In complex math problem-solving, GPT-4o performs better.

Claude 3.5 Sonnet: 71.1% accuracy on zero-shot CoT.
GPT-4o: 76.6% accuracy on zero-shot CoT.

Conclusion: GPT-4o is superior for math-heavy tasks.

3. Latency and Speed:

Speed and latency are crucial for real-time applications.

GPT-4o: Average latency is 24% faster than Claude 3.5 Sonnet.
Claude 3.5 Sonnet: Slightly slower, with longer time to first token and fewer output tokens.

Conclusion: GPT-4o leads in speed and responsiveness.

4. Accuracy in Contextual Understanding:

To test contextual accuracy, I compared the models' ability to respond to a prompt about “Pwn Request for GitHub Actions.”

Claude 3.5 Sonnet: Provided an incorrect response.
GPT-4o: Correctly identified it as a vulnerability.

Conclusion: GPT-4o is more accurate in delivering contextually relevant answers.

Pricing Comparison

Claude 3.5 Sonnet:

Free version available with usage limits (around 10 prompts).
Paid API pricing: $3 per million tokens for input, $15 per million tokens for output.
Claude Pro plan: $18 per month for additional features.

GPT-4o (via OpenAI):

ChatGPT Plus: $20/month for full access.
API pricing: $2.50 per million tokens for input.

Conclusion:

Claude offers more flexibility in terms of cost for basic use, while GPT-4o is more suited for professionals needing high-level capabilities and rapid output.

Final Thoughts: Which Model to Choose?

Choose Claude 3.5 Sonnet if:

You need an AI that offers creative and human-like responses. It’s ideal for tasks requiring empathy, conversation, and logical problem-solving, such as writing, brainstorming, and summarizing content.
Choose GPT-4o if:

You need a high-performance AI for complex tasks involving math, coding, and advanced reasoning. GPT-4o is more robust for professionals dealing with intricate, multi-modal tasks and real-time applications.

Read full article here

4 Techniques for Effective Prompt Engineering

Nik L. — Mon, 06 Jan 2025 07:35:13 +0000

In Casino Royale (2006), James Bond specifies the precise ingredients for his Vesper martini: 3 measures of Gordons, 1 of Vodka, half a measure of Kina Lillet, shaken over ice with a thin slice of lemon peel.

By detailing the components, Bond ensures a superior drink. Similarly, in interacting with large language models (LLMs), the specificity and clarity of your prompts determine the quality of the output.

This process, known as prompt engineering, is essential for obtaining accurate and relevant responses from AI systems.

What is Prompt Engineering?

Prompt engineering involves crafting precise instructions to optimize LLM responses. The more detailed and specific the input prompt, the more relevant and aligned the output will be with your needs.

Example:
If you ask an LLM, “Summarize prompt engineering,” the response may be vague. However, by specifying the request like this:

Prompt: “Give a 100-word summary of prompt engineering, aimed at non-technical users.”

The LLM is now guided to give a concise, audience-specific answer.

Output:

"Prompt engineering is crafting effective instructions for AI systems like ChatGPT to get the best results. It involves being specific, providing context, and breaking down complex questions. By specifying formats, providing examples, or setting boundaries, prompt engineering ensures clear communication with an AI assistant."

This method of asking for specific outputs demonstrates how to "engineer" a prompt to get a desired, precise result.

Components of a Good LLM Prompt

1 Context: The context provides the LLM with additional information that helps it understand your request more fully. By embedding relevant context, the AI can generate more accurate and tailored responses.

Example:
Suppose you are working on a C# project and want to modify a class definition. The context here could be a code snippet.

Prompt with Context:
"Given this C# code:

public class User { public int UserId { get; set; } public string Name { get; set; } public string Email { get; set; } public string PhoneNumber { get; set; } }

Modify the class to make UserId and Name read-only and set them in the constructor."

Output:

public class User
{
    public int UserId { get; }
    public string Name { get; }
    public string Email { get; set; }
    public string PhoneNumber { get; set; }

    public User(int userId, string name)
    {
        UserId = userId;
        Name = name;
    }
}

This example illustrates the importance of providing relevant context to guide the LLM in generating the correct response.

User Question: The question is the main part of the prompt. It should be single-purpose, specific, and concise.

Example:
If you want to create a user class in C# with certain fields, specify the required fields and behavior clearly.

Vague Question:

"Create a user class."

Specific Question:

"Create a C# user class with fields: UserId, Name, PhoneNumber. Make UserId read-only and add a constructor to set these fields."

Output:

public class User
{
    public int UserId { get; }
    public string Name { get; set; }
    public string PhoneNumber { get; set; }

    public User(int userId, string name, string phoneNumber)
    {
        UserId = userId;
        Name = name;
        PhoneNumber = phoneNumber;
    }
}

Output Guidance: You can guide the model’s output by providing examples of the format you want.

Example:
If you need to generate dummy data for a User class, provide an example of what the data should look like.

Prompt with Examples:
"Generate 5 instances of the User class with these fields: UserId, Name, Email, PhoneNumber. Use the following format for examples:

var user1 = new User(1, "John Doe", "john.doe@example.com", "555-555-5555");

Here are some examples:

var user2 = new User(2, "Jane Doe", "jane.doe@example.com", "555-555-5556");

Now generate 5 instances."

Output:

var user1 = new User(1, "John Smith", "john.smith@example.com", "555-555-5555");
var user2 = new User(2, "Jane Doe", "jane.doe@example.com", "555-555-5556");
var user3 = new User(3, "Mary Johnson", "mary.johnson@example.com", "555-555-5557");
var user4 = new User(4, "David Lee", "david.lee@example.com", "555-555-5558");
var user5 = new User(5, "Linda White", "linda.white@example.com", "555-555-5559");

Techniques for Effective Prompt Engineering

Zero-shot Prompting: The LLM generates a response based on its training data without explicit examples. This is effective for generating generic solutions or answers based on established patterns.

Example:
Prompt: "Create a unit test for the User class using xUnit in C#."

Output:

public class UserTests
{
    [Fact]
    public void UserConstructor_SetsProperties()
    {
        // Arrange
        var user = new User(1, "John Doe", "john.doe@example.com", "555-555-5555");

        // Act & Assert
        Assert.Equal(1, user.UserId);
        Assert.Equal("John Doe", user.Name);
        Assert.Equal("john.doe@example.com", user.Email);
        Assert.Equal("555-555-5555", user.PhoneNumber);
    }
}

Few-shot Prompting: Provide several examples to guide the model in generating the desired output format.

Example:
To generate data for a User class, you might use a few example data points to guide the output format.

Prompt with Few-shot Examples:
"Here are some instances of the User class:

var user1 = new User(1, "John Smith", "john.smith@example.com", "555-555-5555");

var user2 = new User(2, "Jane Doe", "jane.doe@example.com", "555-555-5556");

Now create 3 more instances following the same pattern."

Output:

var user3 = new User(3, "Alice Brown", "alice.brown@example.com", "555-555-5557");
var user4 = new User(4, "Bob Green", "bob.green@example.com", "555-555-5558");
var user5 = new User(5, "Charlie White", "charlie.white@example.com", "555-555-5559");

Prompt Chaining: Iteratively refine your queries based on previous responses, allowing the model to build on earlier interactions.

Example:
Start with a simple prompt and progressively modify it.

First Prompt:

"Create a basic User class in Python."

Output:

class User:
    def __init__(self, username, email, password):
        self.username = username
        self.email = email
        self.password = password

Follow-up Prompt:

"Convert this class to C#."

Output:

public class User
{
    public string Username { get; set; }
    public string Email { get; set; }
    public string Password { get; set; }
}

Final Prompt:

"Make the Password property private, and add a DateTime CreatedAt property initialized in the constructor."

Output:

public class User
{
    public string Username { get; set; }
    public string Email { get; set; }
    private string Password { get; set; }
    public DateTime CreatedAt { get; private set; }

    public User(string username, string email, string password)
    {
        Username = username;
        Email = email;
        Password = password;
        CreatedAt = DateTime.Now;
    }
}

Chain-of-Thought Prompting: Provide multi-step instructions, helping the LLM approach a complex problem step-by-step.

Example:
Prompt:

*"Create a set of unit tests for the following C# class:

public class User { public int UserId { get; set; } public string Name { get; set; } public string PhoneNumber { get; set; } }

Think step-by-step:

Identify key scenarios to test.
Write unit tests using xUnit.
Consider edge cases."*

Output:

public class UserTests
{
    [Fact]
    public void UserConstructor_SetsProperties()
    {
        // Arrange
        var user = new User(1, "John Doe", "555-555-5555");

        // Act & Assert
        Assert.Equal(1, user.UserId);
        Assert.Equal("John Doe", user.Name);
        Assert.Equal("555-555-5555", user.PhoneNumber);
    }

    [Theory]
    [InlineData("555-555-5555")]
    [InlineData("123-456-7890")]
    public void UserPhoneNumber_ShouldBeValid(string phoneNumber)
    {
        // Arrange
        var user = new User(2, "Jane Doe", phoneNumber);

        // Act & Assert
        Assert.Equal(phoneNumber, user.PhoneNumber);
    }

    [Fact]
    public void UserPhoneNumber_ShouldThrowException_WhenNull()
    {
        // Arrange
        var user = new User(3, "Invalid User", null);

        // Act & Assert
        Assert.Throws<ArgumentNullException>(() => user.PhoneNumber = null);
    }
}

The article was originally written by Jim, head of Devreal at Pieces for Developers. You can find more examples and nuances in this article https://pieces.app/blog/llm-prompt-engineering

VS Code + LLM = ?

Nik L. — Sat, 04 Jan 2025 14:56:53 +0000

If you're a developer looking to supercharge your workflow, streamline code documentation, or get real-time AI assistance, the Pieces OS VS Code Extension might just be your new best friend. Let’s dive into its features, how to get started, and why it’s worth integrating into your daily coding routine—all in a conversational tone, of course!

What is the Pieces OS VS Code Extension?

Imagine having a coding buddy inside your Visual Studio Code editor—one that helps debug, documents code, and even answers complex coding questions without skipping a beat. That’s the Pieces OS VS Code Extension for you!

It pairs seamlessly with Pieces OS, the powerhouse that drives its capabilities, and integrates AI tools like the Pieces Copilot to bring generative AI directly to your editor. From debugging and refactoring to explaining code snippets, it’s designed to make coding smoother and more intuitive.

Getting Started is a Breeze

Prerequisites: What You’ll Need

Pieces OS – The engine behind the magic. You’ll need to install this on your machine.
VS Code – Make sure you’ve got Visual Studio Code up and running.

While Pieces OS is mandatory for the extension to work, the Pieces for Developers Desktop App is highly recommended for added functionality.

Installing the Extension

Open VS Code and head to the Extensions tab.
Search for "Pieces for VS Code" and click Install.
Restart VS Code, and you’re good to go!

Prefer manual installation? No problem. Grab the .VSIX file from the VS Code Marketplace and install it with a few clicks.

The Powerhouse: Pieces Copilot

At the heart of the extension lies the Pieces Copilot, a feature-packed AI assistant that’s always ready to help. Let’s explore some of its coolest tricks:

1. Code Documentation Made Easy

Keeping your code well-documented is a developer’s dream (and often a nightmare). Pieces Copilot takes the pain out of this process by generating clear and insightful comments. Simply select your code, right-click, and choose Comment Selection with Copilot.

It analyzes the code’s functionality, generates comments, and even lets you insert them at your cursor with a single click. It’s perfect for collaborative projects and maintaining consistency.

2. Debugging in Style

Debugging doesn’t have to be tedious. With the Code Debugging feature, Pieces Copilot highlights issues in your code, suggests fixes, and provides detailed explanations.

Look for the lightbulb icon near an error in your code. Click Pieces: Fix, and voila—solutions at your fingertips!

3. Context-Aware Conversations

Got a question about your code? Pieces Copilot thrives on context. Whether it’s a snippet, an active file, or your entire workspace, it can give you tailored answers.

Ask About Selection: Highlight code and ask specific questions.
Ask About Active File: Get insights about the file you’re working on.
Ask About Workspace: Ideal for large projects, this feature analyzes your entire workspace for patterns, inconsistencies, or solutions.

4. Generative AI Conversations

Stuck on a coding problem? Start a chat with Copilot using simple commands or right-click options. It supports in-depth conversations, where you can even add error messages or additional context for more precise answers.

5. Code Extraction from Screenshots

This feature is pure magic. Upload a screenshot containing code, and Copilot extracts it for you. It’s a game-changer when working across different platforms or referencing old projects.

Enhanced Flexibility with Runtime Selection

With Pieces Copilot, you can choose between various Large Language Models (LLMs) depending on your task. Need speed for quick answers? Opt for a lightweight model. Tackling a complex challenge? Use an advanced model for deeper analysis.

Daily Workflow Enhancements

Here’s how Pieces OS VS Code Extension makes your day-to-day work better:

Collaborative Coding: Standardize code and improve readability with intelligent suggestions.
Quick Prototyping: Generate and refine code faster with AI-powered tools.
Skill Building: Learn as you go with explanations and suggestions tailored to your code.
Effortless Refactoring: Modify and optimize code snippets without leaving your editor.

Going the Extra Mile: Optional Pieces Cloud

Want to back up your snippets or share links with your team? Connect to the Pieces Cloud. Prefer staying offline? That’s fine too—the core functionalities work without an account.

Why Developers Love It

The Pieces OS VS Code Extension isn’t just a tool; it’s a productivity booster that simplifies even the most complex tasks. Whether you’re a solo developer, part of a large team, or just starting out, it offers features that adapt to your needs and grow with your skills.

Ready to take your coding game to the next level? Install the Pieces OS VS Code Extension today and watch your productivity soar!

DEV Community: Nik L.

The Rise of On-Device AI and the Return of Data Ownership

1. Why we left the cloud

2. A faster, leaner, smarter AI stack

3. It’s not just us, this is an industry shift

4. How on-device AI actually works?

5. Privacy isn’t a feature, it’s the architecture

6. Cloud costs vs. fixed compute

7. Where on-device AI shines

8. So what now?

🧠 Pieces AI Memory: Built for Real Developer Workflows

🔁 Always-On Context

🧠 Long-Term Memory—Engineered for Developers

🔌 Built Into Your Stack

Supported Platforms Include:

🧩 From Assistant to Operating System

🚀 Built to Evolve With You

🔍 Learn More

We Fine-Tuned our OCR to Read Code: Here’s What It Took (and What Broke)

What is Optical Character Recognition?

OCR for Code at Pieces

OCR Engine Choice: Tesseract + LSTM

Image Pre-Processing for Code Screenshots

Key Challenges & Solutions

1. Dark Mode and Color Inversion

2. Noisy or Gradient Backgrounds

3. Low-Resolution Images

Post-OCR: Code Layout and Indentation Inference

Layout Inference Strategy:

Evaluation Methodology

Evaluation Metrics:

Summary: Tailoring OCR for Code is Non-Trivial

Get Started with Pieces OCR

Related Technical Articles

Top 10 AI Models Every Developer Should Know in 2025

How I selected the models in this list

Top 10 AI Models for Developers in 2025

1. GPT-4o: The Versatile Creator

2. Claude 3.7 Sonnet: The Developer's Companion

3. YOLO (You Only Look Once): Computer Vision Workhorse

4. BERT: The NLP Foundation Model

5. LLaMA: Open Source Foundation

6. Whisper: Audio Intelligence

7. XGBoost: The ML Reliable

8. Stable Diffusion: Visual Creation Engine

9. Mistral 7B: The Efficient Performer

10. Granite 3.0: Enterprise-Ready AI

Making the Right Choice: A Developer's Decision Framework

Conclusion: Build Your AI Stack Strategically

Nano-Models for Temporal AI - We created this new breakthrough to offload temporal understanding entirely to local hardware

⚡ Nano-Models for Temporal AI: Pieces’ LTM-2.5 Breakthrough

🧠 The Breakthrough: LTM-2.5

🛠️ Why It Works

🔍 What It Unlocks

🔥 Introducing Pieces MCP Server: Your AI Tools Just Got a Memory Upgrade of 9 Months Context Window

Memory for Your Favorite Dev Tools

⚙️ What It Actually Does (TL;DR)

🔧 How to Get Started

🛠️ Why It’s Built Different

🧪 What Can You Ask?

🕵️ Under the Hood (For the Curious)

🔍 Feature Highlights

💸 Token Costs & Tips

🚀 Ready to Try It?

I Tested 50+ LLMs and The Results Were Surprising.

🛠️ Testing Setup

My Hardware

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⚡ Fastest Models

⏳ Fastest to First Token (Cloud)

🚀 Fastest Local Model

🎯 Most Accurate Models

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🤔 Why Do LLMs Perform So Differently?

🏅 Overall Winner: GPT-4o (OpenAI)

Scoring System

🔍 So… What Should You Use?

My Personal Picks

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🍸 What’s Prompt Engineering Anyway?