Northstar.Coding

Experienced Full Stack Blockchain Engineer and founder of NorthStar Coding, specializing in responsive design, app development

[Northstar.Coding @ib310us]

🚀 𝗝𝘂𝘀𝘁 𝗟𝗮𝘂𝗻𝗰𝗵𝗲𝗱: "𝗕𝗶𝗹𝗹𝗶𝗲 𝘁𝗵𝗲 𝗕𝗲𝗮𝗿" – 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗕𝗶𝗴 𝗧𝗵𝗶𝗻𝗴 𝗶𝗻 𝗖𝗿𝘆𝗽𝘁𝗼! 🐻💎

I'm beyond excited to share the animated video trailer and website I just created for Billie the Bear – the most adorable (and genius) crypto project you'll see this year! 🎥✨

The team behind this is next-level brilliant, and I have a feeling this is going to be 𝗛𝗨𝗚𝗘. Billie isn’t just another meme coin—this bear has vision, charm, and a roadmap that’s going to shake up the space. 🚀

🔥 Why Billie?
🐻 A lovable mascot with viral potential
💡 Rockstar devs & marketing geniuses behind it
🌐 Sleek, engaging website & trailer (if I do say so myself 😉)
This is your early invite to the den—don’t sleep on it!

👉 Watch the trailer (attached), visit the site, and get ready for the roar.

hashtag#BillieTheBear hashtag#Crypto hashtag#NextBigThing hashtag#MemeCoinSeason hashtag#Blockchain
LFG! 🚀🐻
(Like, comment, and share if you’re bullish on Billie!)

P.S. Tagging the hashtag#CryptoCommunity – who’s joining the Billie movement? 🚀🔥

[Northstar.Coding @ib310us]

Hey @here
Looking to hire a full time full stack developer(React, tailwindCSS) for a full-time position in our team.
Reply here if you're interested. It will be a great opportunity!

#development #HiringAlert #mobile

[Northstar.Coding @ib310us]

"Swift Programming and Poetic Words"

As the King leads the fate of the board,

he invests wholly in his Queen.

He trusts her with every move

for loyalty strengthens the entire team.

The King moves one square at a time,

building steady value for his Queen.

The Queen, with greater reach,

may choose any square she pleases.

But if she invests in a pawn each turn,

She risks losing the support of her King.

The queen may go anywhere on the board,

But the King decides the fate of the game.

And if the King refuses to play the game,

He ends it on his terms, not hers.

And if the Queen still chooses the pawn

throughout the game,

The King will wear his crown

Without her on the board.

[Northstar.Coding @ib310us]

Prompt engineering is NOT always enough.

Neither is zero-shot. Neither is few-shot.

If your LLM isn't performing in your specific domain,
you’re probably overdue for finetuning.

Here’s how to know if it's time—and how to do it right 👇

• General vs. Domain-Specific – Public models struggle with niche vocab and workflows (think: medical, legal, finance).
• Speed vs. Accuracy – You might get fast answers, but not the right ones.
• Privacy vs. Public Data – If your use case involves sensitive info, prompts won’t cut it.
• Scalability – Manual prompting doesn’t scale across thousands of queries or users.

So, how do you actually approach finetuning?
Here’s a clear framework:

🔹 Step 1: Identify the gap
– Where is the base model failing? Misclassification, hallucinations, poor tone?

🔹 Step 2: Choose your method
– Unsupervised: Use raw domain text
– Supervised: Train on labeled examples
– Instruction-based: Guide it with plain-language tasks

🔹 Step 3: Pick your tuning strategy
– Full Finetuning → Maximum power, maximum cost
– Adapter-based → Plug-in modules, less training overhead
– PEFT → Train just 15-20% of the model, high ROI
– RLHF/DPO → Optimize for human preferences

🔹 Step 4: Train on your actual data
– Not open web data. Not synthetic samples.
– Your support emails, medical records, legal docs, or internal docs.

Best practices?
🔸 Use synthetic data to augment rare edge cases
🔸 Don’t skip human-in-the-loop testing
🔸 Monitor output drift over time—models degrade

Want your LLM to behave like a subject-matter expert—not a generic chatbot?

👉 Finetuning is how you teach it your business.

📌 Save this post if you’re building real-world AI systems.
💬 Drop a 🔧 if you’ve started experimenting with finetuning.
♻️ Share with your ML team or technical decision-makers.

hashtag#AI hashtag#LLM hashtag#Finetuning hashtag#PromptEngineering hashtag#EnterpriseAI hashtag#MachineLearning hashtag#AIforBusiness hashtag#GenAI hashtag#NLP hashtag#TechLeadership

[Northstar.Coding @ib310us]

Looking to hire a full time Mobile developer with opportunity for a full-time position in our team.
reply here if you're interested.
It will be a great opportunity!

#development #HiringAlert #mobile

[Northstar.Coding @ib310us]

Breaking Bitcoin’s Speed Limit With Kaspa

Bitcoin’s original blockchain protocol deliberately prioritizes security over speed, using a system called Proof-of-Work to reach consensus. In this model, miners compete to solve complex mathematical puzzles, and the first to succeed earns the right to add the next block to the chain. Satoshi Nakamoto’s design enforces a 10-minute block time to ensure that each new block has time to fully propagate across the network before the next race begins. This helps minimize “orphaned” blocks - valid blocks that arrive too late - but also severely limits throughput and responsiveness, highlighting a fundamental tradeoff between trustless consensus and scalability.

[Northstar.Coding @ib310us]

Kaspa: The Israeli Answer To Scaling Bitcoin

In the ever-evolving crypto landscape, a relatively quiet contender is making waves by rewriting the rules of Proof-of-Work blockchains - Kaspa. A project born from Israeli academic research - Kaspa is leveraging a novel protocol called GHOSTDAG to overcome Bitcoin’s biggest limitation: speed. Invented by Hebrew University researchers Yonatan Sompolinsky, Shai Wyborski, and Aviv Zohar, GHOSTDAG transforms the traditional blockchain into a blockDAG (directed acyclic graph of blocks), allowing parallel blocks and sub-second confirmation times without sacrificing security.

As of May 2025, Kaspa’s KAS token trades around $0.10, giving the network a market capitalization near $2.7 billion - a top-50 crypto that sprang from a university idea into a growing ecosystem.

[Northstar.Coding @ib310us]

Time to Build Your Large Language Models

LLMs have revolutionized the way we interact with and use AI, offering unprecedented capabilities in NLP and more. Whether you’re leveraging APIs for quick integration or managing open-source models for greater control, understanding how these models work and the best practices for their deployment is crucial.

As enterprises explore these technologies, tools like prompt engineering, RAG, and instruction-driven querying become essential in maximizing the potential of LLMs while mitigating risks. By integrating these strategies, businesses can harness the full power of LLMs, transforming vast data into actionable insights.

[Northstar.Coding @ib310us]

@Shitikyan Sounds great! I’m a full-stack developer with strong experience in Next.js, Tailwind, and some blockchain. Would love to be considered, happy to chat further!

[Northstar.Coding @ib310us]

We hear a lot about AI agents which can book meetings using tools, search the internet, even generate code. And then came an another term Agentic AI. Sounds similar, right? But actually, they are not. Yes while both involve AI doing things for us, the mindset and design behind them are very different. One follows instructions, the other makes its own decisions based on goals.

The difference between AI Agent and Agentic AI is a must know concept.

In this article, we’ll break down what really sets them apart and why this difference is a big deal to understand in AI.

What are AI Agents?
An AI agent is a system that:

Perceives its environment (through input like text, images, audio, etc.),
Thinks or reasons (uses AI models or logic to understand),
Acts to achieve goals (responding, performing actions, generating results)
Example:
While an LLM can generate code, we can equip it with a code interpreter tool. This allows it to not only write code but also run it and respond with the computed result (reduces hallucination).
So if we ask, “Find the 345th Fibonacci number,” the agent:

Writes the code.
Executes it using the interpreter,
And returns the computed answer accurately.
This makes the agent truly interactive, tool-augmented, and goal-orienteda core characteristic of agentic AI.

In Short: A single agent has access to multiple tools.

Key Features:

Reactive, responding to predefined triggers or user requests.
Limited autonomy and learning
Often powered by Large Language Models (LLMs as brain) with tools (custom functions) or evolving toward Specialized Language Models (SLMs) for specific tasks.

Source: Image by author
What Is Agentic AI?
Agentic AI represents recent advanced form of artificial intelligence that operates autonomously. It can make decisions, set its own goals, and adapt to new situations with minimal or sometimes without human guidance.

In a multi-agent system powered by Agentic AI, small- to medium-scale SaaS applications can be developed by a coordinated crew of specialized AI agents.

Each agent is designed for a specific role and equipped with appropriate tools. The Coder uses an LLM optimized for programming along with a code interpreter to write and execute code.

The Researcher relies on a general-purpose LLM connected to internet search tools to gather relevant documentation, libraries, and best practices.

The Reviewer uses an LLM fine-tuned for code review to catch bugs, ensure code quality, and flag security issues. The Enhancer integrates improvements, manages dependencies, and optimizes performance by accessing both the codebase and terminal.

Lastly, the Feedback Handler or Tester creates and runs test cases using testing frameworks to validate the system and report errors. These agents perceive their environment, reason across tasks, and act in a coordinated, proactive manner.

This structure exemplifies Agentic AI autonomous agents working together to achieve complex goals with minimal human intervention.

[Northstar.Coding @ib310us]

🧠💻 In 2025, coding isn't king — thinking is.

CTOs aren't just asking "Can you code?"
They're asking "Can you tell when AI is lying to you... confidently?"

Welcome to the new job market, where:

AI writes the code 😌

You debug its hallucinations

Top skills companies now want in devs:
✔️ Critical thinking over code slinging
✔️ Systems thinking over syntax
✔️ Business fluency over brute force

They’re not hiring for speed-typers anymore.
They want AI editors, logic auditors, and engineers who treat Copilot like a well-meaning but unreliable intern.

If you can say "Wait... this looks too perfect," you might just land the job.

AI isn’t replacing developers.
It’s just filtering out the ones who don’t ask questions.

—

Think before you code.
Audit before you deploy.
Doubt before you trust AI.

[Northstar.Coding @ib310us]

@akafaceUS give that man a raise!

[Northstar.Coding @ib310us]

@amuse 😂

[Northstar.Coding @ib310us]

@Jere_Memez @cb_doge I'm just amazed how us Jews, people that suffered at the hands of Nazis... have no problems with either expression of celebration. Stop appropriating our history for your political posters... before we ask for reparations 😂

[Northstar.Coding @ib310us]

@rubydiana @cb_doge idiots aren't sown nor reaped

[Northstar.Coding @ib310us]

@ayobamimoses23 fake post, trying to gather comment activity at all cost. the rest of his page starts with meme coin posts, then "trending" topics, Grammy, sports game etc. 4 days ago he claims to have gotten his designers for a new project, now again for 2 days he needs logo designer? lies.

[Northstar.Coding @ib310us]

Your ‘enterprise’ toolkit just went $0. 🤑
Real-time data, AI, team access—no upfront fees.
Only $99.99/mo after trial.
👉 Try it: [https://t.co/JiLSEs8BBY](https://t.co/0dgSCycYU4)
#BootstrapBoss #SaaS

[Northstar.Coding @ib310us]

Check out my latest article: Why the Man Pulled the Snake Out of the Fire, After It Had Bitten Him 🐍🔥 https://t.co/wOxwPlBotC via @LinkedIn