2026-02-11 00:58:41
Hey Hackers!
This article is the first in a two-part series. I want to pull back the curtain on why Physical AI is one of the hardest engineering problems of our generation.
Most software engineers live in a deterministic world.
You write a function. You pass it an input. It returns the exact same output every single time. If it fails, you check the logs. You fix the logic. You redeploy. The server doesn't care if it's raining outside. The database doesn't perform differently because the sun is at a low angle.
Physical AI does not have this luxury. It has to deal with the Body Problem of Artificial Intelligence.
Today’s tech industry obsesses over Large Language Models generating text in data centers. Meanwhile, a quieter but much harder engineering challenge is playing out in the real world. It is happening on our streets with autonomous vehicles. It is playing out in operating rooms, where surgical robots assist with sub-millimeter precision. It is happening in warehouses, where robotic arms sort fragile packages next to heavy machinery.
This is the challenge of Physical AI. Research and industry communities sometimes also call this Embodied A,I which can be inferred as the science of giving an intelligent agent a physical body. I think it does not matter whether you call it Embodied or Physical because the engineering reality is the same. It is the discipline of taking neural networks out of the server and putting them into the chaos of reality.
When we build intelligence for these machines, we leave the clean world of logic gates. We enter the messy, probabilistic world of physics. A sensor might tell you a wall is ahead, but it might actually be a patch of fog or a reflection. The AI has to decide whether to stop or push through.
\
I’m Nishant Bhanot, and I help build these systems for a living.
My work has spanned the full spectrum of autonomy. At Ford, I worked on the core driver-assist technologies that keep millions of consumer vehicles safe on the highway every day. If your car has ever corrected your steering to prevent you from drifting out of your lane, or braked for you because a jogger with earphones suddenly ran in front of your car, you have felt the result of my validation work.
Today, I am a Senior Sensing Systems Engineer at Waymo. My work helps improve the fleet of thousands of fully autonomous vehicles across multiple major US cities like San Francisco, LA, Austin, Miami, and others. I spend my days solving the edge cases that arise when you deploy commercial robots at that scale.
I have learned the hard way that the physical world has a nasty habit of breaking even the most elegant algorithms.
\
If you ask an LLM to write a poem, the input is simple. It is text. Maybe an image or video. A reference to a song at best.
Physical AI, however, requires a deep understanding of unsaid rules and physical properties.
Take, for example, an agricultural robot designed to pick strawberries. A standard vision model interprets a strawberry as just a red cluster of pixels. To a Physical AI, the same strawberry is a red and soft but somewhat pressurized object.
If the robot grips too hard, it crushes the fruit, but if it grips too loosely, it drops it. The AI cannot just see the berry. It has to feel the structural integrity of the object in real-time. It has to understand soft-body physics. This is a level of multimodal sensory processing that a text-generator never has to consider.
This nuance applies to social friction as well. A computer vision model can identify a car at an intersection, but the hard part is understanding why that car is creeping forward. Is the driver aggressive? Are they distracted? Or are they signaling a social contract that they want to turn right?
This applies to humanoid robots, too. If we want robots to cohabit with us, they cannot just track our location but rather read our micro-movements. A slight shift in a person's shoulder might indicate they are about to stand up. If the robot doesn't read that context, it creates a collision.
LLMs deal in tokens. Physical AI deals in social friction and intent.
\
The biggest difference between Software AI and Physical AI is the cost of failure. This cost is driven by two invisible constraints. Physics and Time.
In standard software, a late answer is just an annoyance. If a chatbot takes three seconds to reply, it feels sluggish, but the answer is still correct.
In Physical AI, a correct-but-late answer is a wrong answer.
Imagine a reusable rocket attempting a vertical landing and is descending at 300 meters per second. If an AI guidance algorithm takes just 100 milliseconds to calculate the firing solution, the vehicle has fallen another 30 meters completely uncontrolled. It crashes into the pad before the engines can ever generate enough thrust to stop it. The physics of gravity do not pause while the software thinks.
We don't just optimize for accuracy. We optimize for worst-case execution time. If the control cycle is 33 milliseconds, and the code takes 34, we don't wait. We drop the frame. We have to. This need for speed is driven by the safety stakes.
I once experienced a complete blackout of the infotainment system in a rental car while driving at 70 mph. The screen went dark. The music stopped. It was annoying. I was frustrated. But the car kept driving. The brakes worked. The steering worked. The failure was confined to a screen.
I see a similar pattern with modern LLMs. I pay for high-end subscriptions to the best models available. Time and again, they provide me with "facts" that are sometimes unverified hallucinations. It is a waste of my time, and it breaks my workflow. But nobody gets hurt.
Physical AI does not provide this safety net. I can keep thinking of multiple scenarios where decisions of Physical AI bear consequences.
If a humanoid robot in your kitchen has a glitch, it doesn't just display an error message. It drops your expensive china. It knocks over a boiling pot. I am pretty sure you would not want your robot butler to dart across the hallway at high speeds.
If an autonomous vehicle decides to drive the wrong way down a one-way street, it isn't a bug, it is a significantly unsafe situation.
If a surgical robot’s vision system misinterprets a shadow and moves the scalpel three millimeters to the left, there is no undo button. It doesn't throw an exception; rather, it unfortunately severs a nerve.
In the world of Software AI, a fix to a failure is a restart away. In the world of physical AI, fixing failures is most definitely not as simple as a restart. This fundamentally changes how we engineer these systems. We cannot afford to move fast and break things when the actions can lead to physical harm.
\
Because the consequences of failure are so high, we face a paradox.
In almost every other discipline, you learn by doing. If you want to learn to ride a bike, you wobble and fall. If you want to test a new database schema, you run it and see if it breaks. Failure is part of the learning loop.
In Physical AI, learning by doing is rarely an option.
You cannot deploy untested code to a robotic arm handling radioactive waste just to see what happens. You cannot crash a billion-dollar satellite to teach it how to dock. We have to train these systems for the physical world without actually being in the physical world.
This forces us to rely on Digital Twins. We build physics-compliant simulations that replicate gravity, friction, light refraction, and material density. We have to build a Matrix for our machines.
But the simulation is never perfect. There is always a Sim-to-Real gap. A simulated floor is perfectly flat. A real warehouse floor has cracks, dust, and oil spots. If the AI overfits to the perfect simulation, it fails in the imperfect reality. We end up spending as much time validating the simulation as we do validating the AI itself.
\
This brings us to the final hurdle of the Validation Cliff.
In web development, "Five Nines" (99.999%) of reliability refers to server uptime. If your server goes down for 5 minutes a year, you are a hero.
In Physical AI, 99% reliability is a disaster.
If a robot makes a correct decision 99 times out of 100, that means it fails once every 100 interactions. For a fleet of millions of personal humanoid robots making thousands of decisions per second, that error rate is mathematically unacceptable.
We have to validate for the "Long Tail." We have to engineer for the weirdest, rarest events possible. The glare of the sun hits a camera at the exact wrong angle. A person wearing a T-shirt with a stop sign printed on it.
This is why Physical AI is the next great frontier. We have mastered the art of building models for processing information. Now we have to refine them such that they always exhibit safe physical behavior.
\
We have established that Physical AI is hard because the inputs are nuanced, time is scarce, and the stakes are fatal. But there is a bigger misconception holding the industry back.
There is a belief that to be safe, our robots should think and see like humans.
In Part 2, I will argue why this is a dangerous myth. I will explain why Human-Level accuracy is actually a failure state, and why the future of safety relies on machines that are decidedly superhuman. Stay Tuned!
2026-02-11 00:52:43
AI-to-e-commerce traffic grew by roughly 4,700% year-over-year in 2025. Morgan Stanley estimates that agentic shoppers could capture between $190 billion and $385 billion in US e-commerce by 2030. Two protocols are now competing to define how AI agents actually transact online, and both are running into the same infrastructure wall.
\
Universal Commerce Protocol (UCP) is an open standard developed by Shopify in collaboration with Google. Over 20 major retailers back it, including Target, Walmart, Best Buy, plus payment processors like Visa, Mastercard, and Stripe.
The protocol standardizes how AI agents discover products, manage carts, complete checkouts, and handle post-purchase flows like returns and order tracking. Before UCP, any developer building an AI shopping agent needed custom integrations for every merchant. UCP removes that friction by defining universal primitives that map to standard retail operations.
On the transport layer, UCP supports REST, GraphQL, JSON-RPC, MCP (Model Context Protocol), and Agent-to-Agent communication. Merchants and agents declare their supported capabilities, and UCP negotiates the differences automatically. Shopify's engineering team built the protocol on top of data from billions of transactions processed across their merchant network.
Google's UCP integration enables purchases directly within Gemini, AI Mode in Search, and Google Shopping. Merchants can choose between native checkout (direct API integration) or embedded checkout (iframe-based) depending on how much control they want over the buyer experience. In both cases, the merchant remains the Merchant of Record, keeping customer relationships, data ownership, and post-purchase control.
\
x402 takes a narrower approach. Developed by Coinbase, it doesn't handle commerce workflows at all. It handles payments, specifically turning HTTP's long-dormant 402 status code ("Payment Required") into an actual payment trigger.
The protocol spec is open source and the flow works like this:
1. Agent requests a paid resource (API endpoint, content, service)
2. Server returns HTTP 402 with payment address, amount, and currency
3. Agent wallet signs a USDC transaction
4. Agent retries the request with payment proof in headers
5. Server validates on-chain settlement - returns the resource
Settlement happens on-chain in 4-8 seconds with zero protocol fees. This makes x402 well-suited for micropayments, pay-per-request API access, content unlocking, and agent-to-agent payments, use cases where traditional payment rails are either too slow or too expensive.
\
UCP and x402 are not competitors - they solve different problems.
| | UCP | x402 | |----|----|----| | Scope | Full commerce workflow | Payment settlement only | | Built by | Shopify (with Google) | Coinbase | | Payment method | Negotiated (Shop Pay, cards, etc.) | USDC on-chain | | Settlement | Varies by payment handler | 4-8 seconds | | Protocol fees | Standard processing fees | Zero | | Best for | Retail product purchases | APIs, micropayments, pay-per-request |
An agent could theoretically use UCP for product discovery and cart management while using x402 for payment if a merchant supported both, but the protocols weren't designed as a required pairing. UCP already has its own payment handling through Shop Pay and negotiated payment handlers.
\
Both protocols work on paper. The technical specs are sound, the backing is strong, and early implementations are live. But there's a shared infrastructure problem that neither protocol addresses directly: anti-bot detection.
UCP merchants and x402 endpoints both deploy bot detection systems - they have to. The same open protocols that let AI agents complete legitimate purchases also attract scrapers, credential stuffers, and inventory bots. Merchants can't reliably tell the difference between a helpful AI agent and a malicious one, so the default response to anything that looks automated is to block it.
This creates a real deployment gap between "protocol works in a test environment" and "protocol works at scale in production."
Agents running from AWS, GCP, or DigitalOcean IP ranges trigger bot detection systems almost immediately. UCP discovery queries get rate-limited within minutes. x402 payment endpoints flag cloud hosting ASNs. Transaction success rates from datacenter IPs typically land between 15-25%, with blocks kicking in after 2-5 minutes.
Residential proxy pools improve success rates, but IP geolocation mismatches still trigger fraud checks. Shared pools carry reputation risk - other users' bad behavior contaminates your IPs. CAPTCHA challenges interrupt agent workflows that need to be fully autonomous.
Mobile proxies from real 4G/5G carrier networks (Verizon, T-Mobile, AT&T, and equivalents globally) produce traffic that's indistinguishable from legitimate smartphone users. CGNAT (Carrier-Grade NAT) means these IPs are naturally shared with thousands of real mobile users at any given time, so merchants can't block the ranges without blocking real customers.
Reported success rates on mobile carrier IPs sit around 85-95%, with sessions holding stable for 30+ minutes and CAPTCHA trigger rates below 5%.
\
Both UCP and x402 workflows span multiple sequential HTTP requests. A UCP purchase goes through discovery → cart → checkout → payment confirmation. An x402 flow goes through resource request → 402 response → payment submission → access grant.
If the proxy rotates mid-workflow, merchants see requests from different geographic locations hitting the same session token. That reads as a compromised account - a classic fraud signal. The agent gets blocked not because it's doing anything malicious, but because the IP switching pattern looks identical to credential stuffing.
The fix is sticky sessions: maintaining the same proxy IP for the full duration of a workflow. For UCP retail transactions, that typically means 10-30 minute sessions. For x402 micropayments, 5-10 minutes is usually enough since the flow completes faster.
A simplified proxy configuration pattern for a UCP agent:
import requests
class UCPAgent:
def __init__(self, proxy_url: str):
self.session = requests.Session()
self.session.proxies = {
'http': proxy_url,
'https': proxy_url
}
self.session.headers.update({
'X-Session-Duration': '1800',
'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 17_0 like Mac OS X)'
})
def discover_products(self, query: dict) -> dict:
return self.session.post(
'https://merchant.example/ucp/v1/products/search',
json=query
).json()
def checkout(self, cart_id: str, payment: str) -> dict:
return self.session.post(
'https://merchant.example/ucp/v1/checkout',
json={'cart_id': cart_id, 'payment_method': payment}
).json()
The key detail: self.session keeps the same proxy connection from discovery through checkout. Rotation should happen between sessions, not during them.
\
Several providers offer mobile proxy infrastructure that can support these workflows, each with different trade-offs.
Bright Data and Oxylabs are the largest enterprise players, with IP pools exceeding 150M+ and 175M+ respectively. Both offer shared mobile proxy pools with extensive geo-targeting, well optimized for high-volume scraping and data collection. Their AI tooling focuses on scraper APIs and data extraction rather than full proxy access for autonomous agents. Smartproxy (now rebranded as Decodo) sits in the mid-tier with more accessible pricing and a simpler interface, solid for teams that don't need the full enterprise feature set.
The challenge for agent commerce specifically is that shared pools rotate IPs, carry reputation damage from other clients, and cap sticky sessions at 10-30 minutes. AI agents need human-like connectivity: IPs that don't get burned, sessions that hold for hours, and programmatic control over the proxy layer. Providers like VoidMob address this with dedicated mobile proxies on real 4G/5G carrier connections, 24-hour stable IP sessions, and full MCP server access for proxy management, so the agent itself can select and control its infrastructure as part of the workflow.
The right choice depends on the use case. Shared pools handle data collection fine. Dedicated mobile infrastructure is what agent commerce demands when every transaction needs to look like a single, consistent user from start to finish.
\
Teams deploying agent commerce workflows keep hitting the same set of issues:
"Security check" errors during UCP checkout typically trace back to IP rotation mid-session. Some proxy providers silently rotate IPs when connections drop, which breaks the session continuity merchants expect.
x402 payment accepted but resource still blocked happens when payment validation and bot detection are separate systems. The USDC transaction confirms on-chain, but the request still gets flagged because the originating IP is a datacenter ASN. These are independent layers, passing one doesn't guarantee passing the other.
Rate limiting on UCP discovery queries results from either too many requests from one IP or a flagged ASN. Real users don't fire 50 product searches per second. Adding realistic delays and using carrier ASNs instead of hosting ASNs resolves most cases.
CAPTCHA challenges interrupting autonomous flows usually indicate degraded proxy pool reputation. Shared pools carry cumulative risk from other users' activity. Dedicated mobile IPs with a clean history cost more but eliminate the interruptions.
\
UCP and x402 represent two distinct bets on the future of agent commerce. UCP is an ecosystem play, with Shopify and Google building standardized rails across their massive merchant networks, traditional payment processing, and full workflow coverage. x402 is an open protocol play, where any server can accept crypto payments through standard HTTP semantics, no integration meetings required.
Both are technically ready. The $190-385 billion market opportunity Morgan Stanley projects isn't blocked by protocol design. It's blocked by the gap between how these protocols assume agent traffic will be treated and how merchant infrastructure actually handles it.
The teams that figure out the proxy and session management layer first will be the ones that ship working agent commerce products. Everyone else will be stuck debugging 403 responses.
2026-02-11 00:46:47
Everyone loves clean demos.
Perfectly aligned PDFs. Machine-printed text. Near-100% extraction accuracy in a controlled environment. It all looks like document automation is a solved problem.
Then reality hits.
In real business workflows, handwritten forms remain one of the most stubborn failure points for AI-powered document processing. Names written in cursive, cramped numbers squeezed into tiny boxes, notes crossing field boundaries: this is the kind of data companies actually deal with in healthcare, logistics, insurance, and government workflows. And this is exactly where many “state-of-the-art” models quietly fall apart.
That gap between promise and reality is what motivated us to take a closer, more practical look at handwritten document extraction.
This benchmark features 7 popular AI models:
Azure
AWS
Claude Sonnet
Gemini 2.5 Flash Lite
GPT-5 Mini
Grok 4
\
Most benchmarks for document AI focus on clean datasets and synthetic examples. They are useful for model development, but they don’t answer the question that actually matters for businesses:
==Which models can you trust on messy, real-world handwritten forms?==
When a model misreads a name, swaps digits in an ID, or skips a field entirely, it’s not a “minor OCR issue”: it becomes a manual review cost, a broken workflow, or, in regulated industries, a compliance risk.
So this benchmark was designed around a simple principle:
test models the way they are actually used in production.
That meant:
Real documents, real problems.
We evaluated multiple leading AI models on a shared set of real, hand-filled paper forms scanned from operational workflows. The dataset intentionally included:
Different layout structures and field organizations
Mixed handwriting styles (block, cursive, and hybrids)
Varying text density and spacing
Business-relevant field types such as names, dates, addresses, and numeric identifiers
\
We didn’t optimize for “how close the text looks” at a character level. Instead, we scored extraction at the field level based on whether the output would actually be usable in a real workflow. Minor formatting differences were tolerated. Semantic errors in critical fields were not.
In practice, this mirrors how document automation is judged in production:
Even with the strongest models, handwritten form extraction rarely crosses the 95% business-accuracy threshold in real-world conditions. Not because models are “bad,” but because the task itself is structurally hard:
This benchmark was designed to surface those limits clearly. Not to make models look good, but to make their real-world behavior visible.
\
When we put leading AI models side by side on real handwritten forms, the performance gap was impossible to ignore.
Two models consistently outperformed the rest across different handwriting styles, layouts, and field types:
\
GPT-5 Mini and Gemini 2.5 Flash Lite delivered the highest field-level accuracy on the benchmark dataset. Both were able to extract names, dates, addresses, and numeric identifiers with far fewer critical errors than the other models we tested.
Azure, AWS, and Claude Sonnet showed moderate, usable performance, but with noticeable degradation on dense layouts, cursive handwriting, and overlapping fields. These models often worked well on clean, structured forms, but their accuracy fluctuated significantly from document to document.
Google and Grok 4 failed to reach production-grade reliability on real handwritten data. We observed frequent field omissions, character-level errors in semantically sensitive fields, and layout-related failures that would require heavy manual correction in real workflows. In their current configuration, these models are not suitable for business-critical handwritten document processing.
One important reality check:
Even the best-performing models in our benchmark struggled to consistently exceed 95% business-level accuracy on real handwritten forms. This is not a model-specific weakness: it reflects how structurally hard handwritten document extraction remains in production conditions.
The practical takeaway is simple: not all “enterprise-ready” AI models are actually ready for messy, human-filled documents. The gap between acceptable demos and production-grade reliability is still very real.
\
Once you move from experiments to production, raw accuracy is only one part of the decision. Latency and cost quickly become just as important, especially at scale.
Our benchmark revealed dramatic differences between models on these dimensions:
| Model | Average cost per 1000 forms | |----|----| | Azure | $10 | | Aws | $65 | | Google | $30 | | Claude Sonnet | $18.7 | | Gemini 2.5 Flash Lite | $0.37 | | GPT 5 Mini | $5.06 | | Grok 4 | $11.5 |
\ For high-volume processing, the economics change everything:
| Model | Average processing time per form, s | |----|----| | Azure | 6.588 | | Aws | 4.845 | | Google | 5.633 | | Claude Sonnet | 15.488 | | Gemini 2.5 Flash Lite | 5.484 | | GPT 5 Mini | 32.179 | | Grok 4 | 129.257 |
\ Processing speed varied just as widely:
The benchmark makes one thing very clear: the “best” model depends on what you are optimizing for.
In production, model selection is less about theoretical quality and more about how accuracy, speed, and cost compound at scale.
\
Going into this benchmark, we expected the usual outcome: larger, more expensive models would dominate on complex handwritten forms, and lighter models would trail behind.
That’s not what happened.
Across the full set of real handwritten documents, two relatively compact and cost-efficient models consistently delivered the highest extraction accuracy: GPT-5 Mini and Gemini 2.5 Flash Lite. They handled a wide range of handwriting styles, layouts, and field types with fewer critical errors than several larger and more expensive alternatives.
This result matters for two reasons:
First: It challenges the default assumption that “bigger is always better” in document AI. Handwritten form extraction is not just a language problem. It is a multi-stage perception problem: visual segmentation, character recognition, field association, and semantic validation all interact. Models that are optimized for this specific pipeline can outperform more general, heavyweight models that shine in other tasks.
Second: It changes the economics of document automation. When smaller models deliver comparable, and in some cases better, business-level accuracy, the trade-offs between cost, latency, and reliability shift dramatically. For high-volume workflows, the difference between “almost as good for a fraction of the cost” and “slightly better but much slower and more expensive” is not theoretical. It shows up directly in infrastructure bills and processing SLAs.
In other words, the benchmark didn’t just produce a leaderboard. It forced a more uncomfortable but useful question:
==Are you choosing models based on their real performance on your documents, or on their reputation?==
\
Benchmarks don’t matter unless they change how you build. The mistake we see most often is teams picking a model first — and only later discovering it doesn’t fit their operational reality. The right approach starts with risk, scale, and failure tolerance.
If errors in names, dates, or identifiers can trigger compliance issues, financial risk, or customer harm, accuracy beats everything else.
GPT-5 Mini was the most reliable option on complex handwritten forms. It’s slower and more expensive, but when a single wrong digit can break a workflow, the cost of mistakes dwarfs the cost of inference. This is the right trade-off for healthcare, legal, and regulated environments.
If you’re processing hundreds of thousands or millions of documents per month, small differences in latency and cost compound fast.
Gemini 2.5 Flash Lite delivered near-top accuracy at a fraction of the price (~$0.37 per 1,000 forms) and with low latency (~5–6 seconds per form). At scale, this changes what’s economically feasible to automate at all. In many back-office workflows, this model unlocks automation that heavier models make cost-prohibitive.
If your documents are mostly structured and written clearly, you don’t need to pay for “max accuracy” everywhere.
Mid-tier solutions like Azure and AWS performed well enough on clean, block-style handwriting. The smarter design choice is often to combine these models with targeted human review on critical fields, rather than upgrading your entire pipeline to a more expensive model that delivers diminishing returns.
Model rankings are not universal truths. In our benchmark, performance shifted noticeably based on layout density and handwriting style. Your documents will have their own quirks.
Running a small internal benchmark on even 20–50 real forms is often enough to expose which model’s failure modes you can tolerate, and which ones will quietly sabotage your workflow.
2026-02-11 00:37:25
AI agents are replacing traditional CI/CD pipelines by autonomously debugging tests, deploying code, and triaging production incidents—GitHub Copilot and Azure SRE Agent already do this. The shift promises real velocity gains: backlogs clear themselves, flaky tests get fixed without human intervention, and toil evaporates. But agents introduce opaque failure modes, runaway feedback risks, and auditability gaps.
2026-02-11 00:08:23
In crypto, price trends are often shaped by real usage, not headlines alone. While many investors wait for major announcements or exchange listings, long term growth usually begins earlier. It starts when users move beyond watching charts and begin actively using a platform’s features.
This change in behavior is critical. As people interact with a protocol, provide liquidity, or rely on it everyday on chain activity, short term trading gives way to long term conviction. That shift strengthens network value and creates more stable demand. In the 2026 crypto market, this pattern is starting to appear around a new project that is seeing rising participation and growing attention from investors focused on utility driven growth.
Mutuum Finance (MUTM) is beginning to show early signs of meaningful user engagement rather than passive interest. The project is more than just another token launch. It is a decentralized lending and borrowing hub built on Ethereum, with a growing ecosystem forming around its development.
More than 19,000 holders are already involved, and many are actively following the protocol’s progress rather than simply holding tokens. Some users participate in the project’s 24 hour leaderboard system, which is designed to reward ongoing engagement.
Others are preparing to interact with mtTokens, the protocol’s yield tracking receipt tokens that are already available for testing in its V1 protocol environment. This type of behavior suggests that the community is positioning itself to use the platform as a practical financial tool once it fully launches.
The project has raised over $20.4 million, reflecting confidence in its direction and execution. Rather than being driven purely by speculation, this support appears tied to Mutuum Finance’s progress toward delivering functional on chain lending infrastructure.
Lending and borrowing platforms tend to grow stronger when they encourage repeat use. Users who supply funds to earn yield often return to track performance, while borrowers stay engaged as they manage their positions over time. This ongoing interaction is usually more durable than short term interest driven by hype.
Mutuum Finance is designed with this behavior in mind. The protocol supports pooled lending for fast access to liquidity, while a peer to peer market, planned for later stages, is intended to allow more customized loan terms. The project is currently in its presale phase, with the MUTM token priced around $0.04 and a confirmed launch price of $0.06. Since Phase 1, the token price has increased by 300%, reflecting growing interest as the platform moves toward broader adoption.
Mutuum Finance (MUTM) reached an important turning point with the launch of its V1 protocol on the Sepolia testnet. Before this release, the project existed mainly as documentation and roadmap goals. With the V1 protocol now live in a test environment, users can actively interact with the system rather than just follow updates.
\ Participants are already testing key functions such as mtToken minting, monitoring positions, and observing how the automated liquidator bot responds to risk conditions. This shift from planning to hands on use is significant, as it helps validate that the core technology works as intended. It also changes how users relate to the project, moving from observation to participation.
Some experts suggest that as more people use the testnet and the mainnet follows as expected, the MUTM price could reach $0.20 to $0.25. This would be a 500% jump from the current level as the project proves its utility.
The final step in shaping user behavior is lowering friction. Mutuum Finance has outlined roadmap plans to improve ease of use and reduce costs as the protocol evolves. These plans include the introduction of a native stablecoin and future Layer 2 integrations, both of which are still under development and not yet live.
Lower transaction fees could allow users to borrow and lend more frequently without cost becoming a barrier. A native stablecoin, once implemented, is intended to support more predictable borrowing and repayment flows.
Together, these upgrades are designed to make the platform more practical for regular use rather than occasional interaction. While adoption at scale will depend on execution and market conditions, these planned features highlight how Mutuum Finance aims to support long term usage growth over time.
Experts believe that as long as these scaling plans succeed, MUTM could reach $1.00 by 2027. This growth is driven by a massive shift in how the world handles finance on the blockchain.
This article is for informational purposes onlyh and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR
Website: https://www.mutuum.com
Linktree: https://linktr.ee/mutuumfinance
:::tip This story was published as a press release by Btcwire under HackerNoon’s Business Blogging Program
:::
\
2026-02-11 00:03:09
How are you, hacker?
🪐 What’s happening in tech today, February 10, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From Is Society Just a Really Complicated Brain? to A Technical Guide to Stealth Addresses and On-Chain Privacy, let’s dive right in.
By @niteshpadghan [ 11 Min read ] Bithumb accidentally created $44B in Bitcoin that didnt exist. Users traded it for 20 minutes. Heres what that reveals about every crypto exchange. Read More.
By @OurAI [ 8 Min read ] Can any system with sufficient complexity have thoughts? Is our society any different? Read More.
By @yaszz [ 21 Min read ] Stealth addresses offer a new path to blockchain privacy—without mixers. Here’s how Umbra, Fluidkey, and Railgun make privacy usable on Ethereum. Read More.
🧑💻 What happened in your world this week?
It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
