• @Dyslexic — Primary public identity
• @DysIexic — Stylized handle using a capital-I substitution
• @Disposal — Historical linked identity
• @disposaI — Historical stylized handle using a capital-I substitution

Dyslexic is the creator and principal operator of Naz API, a Telegram-centered breach-data repository built around massive credential collections, stealer-log material, combo lists, domain lookups, target filtering, and private data sales. Naz API began as a successor-style project to earlier “Naz” material, then evolved into a broader data-access system with bot/API plans, parser work, custom combo orders, targeted line sales, and later Naz v2 resale activity.

The cleanest description is not “elite exploit developer.” It is sharper than that and less cinematic: Dyslexic is a breach-data engineer with a marketplace mouth. His practical value sits in acquiring, sorting, parsing, deduplicating, packaging, and selling large quantities of compromised or scraped data. He understands what buyers want: fresh combos, URL:user:pass material, country-targeted records, salary-filtered leads, full logs, bank/crypto targets, OSINT-style lookups, and “TLO-like” personal-data reports.

Naz API’s power was its boring layer: ingestion, parsing, lookup, filtering, sorting, and resale. That is the part people underrate because it does not look like a skull-mask hacker movie. Bad instinct. Data plumbing is infrastructure. Whoever controls the searchable pile controls the market around it.

The identity style is deliberate: short, ugly, memorable, and typo-proof in the worst way. DysIexic and disposaI both use the same visual trick, swapping lowercase “l” with uppercase “I”. Cute? No. Effective? Annoyingly, yes.

Naz API was a breach-data repository and lookup service. Its advertised and discussed functions included:

• domain-based lookup;
• URL:user:pass search;
• combo generation;
• fresh-data access;
• custom buyer-exclusive combo orders;
• targeted lines by country, date, salary, job, workplace, industry, and related attributes;
• stealer-log-derived data;
• raw database ingestion;
• parsed output formats;
• bot-based access;
• planned or active API access;
• private data sales;
• OSINT-style expansion modules;
• TLO-like personal-data reports.

Dyslexic initially described the name as a meme because the project functioned like “Naz 2.” He then directly claimed to have made Naz API, called himself its developer, and said he had the database on his own drives. Early public sizing put Naz API around 2B lines. Later claims fluctuated: raw working piles were described as tens of billions of lines, while later deduped or canonical Naz counts were described closer to 2.7B. Those numbers should be treated as self-reported scale claims, not audited counts.

The important point is not the exact number. The important point is the model: Naz API took messy stolen or scraped material and turned it into a searchable product.

Naz API was not simply a dump channel. It was closer to a data-access business with several layers.

This is what made Naz API more significant than another random paste pile. A paste pile sits there. Naz API tried to turn breach material into a queryable market.

Naz API and adjacent projects handled or advertised access to:

• URL:user:pass credential lines;
• combo lists;
• fresh stealer-log material;
• domain-targeted credential output;
• email lists;
• bank/crypto target leads;
• LinkedIn-style employment records;
• Facebook-style identity records;
• credit-derived personal-data records;
• names, addresses, phone numbers, emails, salaries, jobs, employers, counties, and industries;
• government-worker or government-adjacent filters;
• TLO-like personal-data reports;
• country-segmented target sets.

Dyslexic repeatedly framed the hard part as sorting and parsing. That tracks. Large breach corpuses are not useful because they are large; they are useful once they are searchable, deduped, normalized, filtered, and packaged. Naz API’s value was not just possession. It was conversion.

The numbers are inconsistent because the categories are inconsistent: raw lines, deduped lines, emails, logs, combos, and target records are not the same thing. Dyslexic talks like someone who knows that distinction but does not always care whether the audience does. Convenient for marketing. Annoying for accounting. Very Telegram.

Dyslexic’s strongest technical lane is data engineering for criminal markets.

Core competencies:

• parsing large breach corpuses;
• deduplicating credential material;
• building simple data-access bots;
• working with API-style lookup access;
• organizing custom combo generation;
• sorting records by useful buyer fields;
• handling raw and parsed database material;
• packaging private data for sale;
• coordinating small dev/database roles;
• understanding buyer demand in fraud, OSINT, and credential markets.

He also claims or discusses offensive capability around exploits, malware, stealer logs, worms, RCEs, droppers, and C2 concepts. Those claims are noisier. Some are plausible as scene experience; some read like flexing; some are likely exaggerated. The reliable part is the data operation. The exploit mythology is less clean.

A fair technical read:

Dyslexic is credible as a data-market builder and parser/operator. He is less cleanly proven as a high-end exploit developer.

Naz API moved through several shapes:

• initial data repository / combo-drop identity;
• Telegram bot concept;
• domain-search and custom-combo service;
• parser-backed data store;
• API-access plan;
• OSINT/TLO-style adjacent service;
• site-based direction when bot reliability became questionable;
• v2 resale/brokerage model.

The architecture was messy but practical. Dyslexic repeatedly complained about rebuilding, unfinished bots, parser work, memory issues, deduplication, and people not working on Naz. That is exactly what a chaotic data service looks like behind the curtain: less “evil genius console,” more “three broken parsers, two unpaid devs, one server bill, and a group chat full of goblins asking when it drops.”

Dyslexic’s sales model combined public attention with private monetization.

Common sales patterns:

• “DMs” for private negotiation;
• custom amounts and filters;
• buyer-requested domains;
• salary-range filtering;
• country-based target selection;
• full-database offers;
• fresh stealer-log combos;
• paid API access;
• monthly lookup-volume pricing;
• free drops to attract users;
• reseller-driven Naz v2 sales.

He understood that different buyers wanted different levels of refinement. Low-end buyers wanted combo lists. Higher-value buyers wanted filtered people, fresh logs, bank or crypto relevance, private access, or API-style lookup volume. Naz API sat between those layers.

Dyslexic presents himself as the originator and central operator, but Naz API was not a one-person fantasy box. He discusses dividing work between bot-side development, database-side work, parser creation, and exploit/data acquisition. He also repeatedly complains about unfinished components, lazy collaborators, and the endless grind of sorting.

The most realistic operational picture:

• Dyslexic created and drove the project.
• Other people contributed bot/database pieces.
• Dyslexic handled or supervised data acquisition, parsing, sales, and direction.
• The project suffered constant rebuilds, delays, and tooling churn.
• Public chat activity doubled as marketing, customer support, reputation management, and chaos control.

Dyslexic’s role was not just “coder.” It was product owner, data broker, loud salesman, annoyed project manager, and janitor of the breach pile. The janitor part matters. The money is in making the pile usable.

Dyslexic’s earliest visible activity is search-bot usage and crude lookup behavior in OSINT-style environments. The pattern is already there: query things, test bot outputs, play with search commands, and treat identity data as something to be retrieved on demand.

This phase is not sophisticated, but it sets the foundation. The later Naz API idea did not appear out of nowhere. It grew from the same instinct: make data searchable, then make searchable data useful.

Under the historical disposaI identity, Dyslexic appears around access-product sales involving government-style emails, EDR-like access, and custom account creation. This establishes an earlier marketplace footprint before Naz API became the main identity center.

The tone is already commercial: product list, price, replacements, buyer contact, and niche access categories. Not polished. Not subtle. But definitely market-aware.

Naz API publicly takes shape in late September 2023. Dyslexic says the project is not yet fully up, describes it as “Naz 2,” says fresh data is being sold, directly claims “I made Naz API,” calls himself its developer, and claims the database is on his own drives. Early size claims place it around 2B lines.

This is the foundation point. Naz API is not just a chat name here; it is presented as a data product with ownership, a database, and a coming API/bot layer.

By October 2023, Naz API is pushing combo drops, including game and food-service themed combos, with plans for a bot that would let buyers order custom combo sets. Dyslexic emphasizes exclusivity: custom data sold to one buyer rather than dumped broadly.

This is where Naz API starts looking like a product instead of a pile. The pitch is simple: ask for a target, domain, or category; get fresh or filtered credential material.

In November 2023, Dyslexic describes enormous raw URL:user:pass holdings and explains that the team has to parse different database formats over time. He distinguishes between raw database lines and parsed combo/log output.

This is a key maturity marker. The operator understands that raw data is not the same as usable data. He talks about parsing formats, dealing with different record types, and converting large dumps into useful search output.

January 2024 is a major development period. Dyslexic talks about rebuilding the bot, sorting by email, URL, date, and country, offering targeted lines, and selling full logs. He also discusses OSINT expansion, TLO-style lookup concepts, credit-derived personal-data corpuses, and lookup-volume pricing.

The project direction becomes broader: not just credentials, but identity intelligence. Naz API begins bleeding into OSINT-as-a-service and people-search tooling. That is the dangerous turn. Credential search is bad enough; full identity enrichment is worse.

By February, Dyslexic describes separate development tracks for Naz and OSINT work, with different people assigned to bot and database pieces. He claims to be the only dev but admits others handle major components, while he focuses on exploit-related work and project direction.

He also makes claims involving exploits and malware-adjacent work. The claims are significant but should be treated carefully. His demonstrated strength remains data infrastructure and marketplace coordination, not cleanly verified exploit authorship.

In March 2024, Dyslexic says the point of Naz is not only money but sorting data. That sentence is one of the best summaries of the whole project. The sales matter, obviously. But the strategic value is the repository: making chaotic breach material searchable enough to monetize repeatedly.

He also pushes back against unrelated promotion in Naz spaces, telling people to work on Naz instead. That reads like an operator trying to keep a chaotic room pointed at the product.

April 2024 shows heavy activity: basic parser creation, unfinished bot work, fresh stealer-log combo sales across multiple chats, data-sales routing, and the clear “Dyslexic” identity marker. The actor says he is “literally dyslexic” and that it is his name; around the same period, the stylized DysIexic handle is used as a point of reference.

April is also the month where high-risk claims spike: fresh stealer logs, custom malware talk, exploit claims, and data for banks/crypto targets. Some of it may be bragging. Some of it is probably not. Either way, it sits around the core Naz business: fresh compromised data in, searchable/targeted products out.

In May 2024, Dyslexic says Naz has around 160K logs at that moment and complains about boredom, unfinished work, and project overload. He discusses possibly shifting away from the bot model and toward a site. He also describes working on many projects at once, including Naz 2, OSINT, and other unrelated builds.

The pattern is classic: talented enough to build useful things, chaotic enough to half-bury himself under unfinished ones. Still, the repository keeps moving.

By July 2024, Dyslexic is involved in reputation disputes, public-dox chatter, and arguments over who has the full database. He acknowledges parts of his personal identity being public but treats much of the surrounding dox material as incomplete or wrong.

This phase shows the social cost of being a visible data-market operator. A person who sells lookup power eventually becomes a lookup target. Shockingly, the leopard did look back.

In August 2024, Dyslexic comments that deduplicating Naz was difficult and later states that Naz was 2.7B. He also directly identifies himself as the owner. This is the late-stage confirmation point: he is no longer only the guy who made it; he is still publicly tied to ownership.

The 2.7B claim likely reflects a more processed or deduplicated view than the huge raw-line claims from 2023. Treat it as a product-scale claim, not a forensic count.

By September 2024, Naz v2 is being sold through a reseller/broker channel, while Dyslexic separately advertises target records by geography and salary range. A country-by-country inventory lists millions of targets across major regions, including the United States, United Kingdom, Mexico, India, Spain, the Netherlands, South Africa, and many others.

This is the mature data-broker phase. The repository is no longer just about “can I find logins for this domain.” It is about filtering people by country, likely income, job context, and target value.

• Large-scale breach-data handling.
• Parser creation and format normalization.
• Credential corpus deduplication.
• Domain-targeted lookup logic.
• Telegram bot/product design.
• API-access planning.
• Buyer-specific combo generation.
• Fresh-data sourcing and packaging.
• OSINT-style enrichment concepts.
• Understanding of fraud-market demand.
• Marketplace timing and promotion.

Dyslexic’s best skill is turning messy compromise material into products people can search, buy, and reuse.

• Chaotic project management.
• Inconsistent claims around scale and capability.
• Reliance on other developers for bot/database pieces.
• Frequent unfinished components.
• Public overexposure.
• Poor separation between product support, bragging, and self-incrimination.
• Uneven credibility on exploit and malware claims.
• Impulsive communication style.

He is not a polished operator. He is productive, messy, loud, and often far too comfortable saying the quiet part in a public room.

Dyslexic’s communication style is abrasive, fast, and hostile. He uses heavy slurs, mocks rivals, complains constantly, and treats chaos as normal operating weather. That ugliness should not distract from the practical competence underneath it. He is rude, but not useless. A lot of people in these scenes are both; he is simply louder about it.

Recurring behaviors:

• public flexing over data size;
• blunt sales posts;
• “DMs” routing;
• argument-driven reputation management;
• dismissing rivals as skids or scammers;
• complaining about unfinished work;
• casually discussing backend problems;
• using free drops as promotion;
• turning every chat into a support desk;
• mixing real technical comments with obvious bravado.

The result is a profile with two layers: the clownish surface and the functional machinery underneath. Do not be fooled by the clowning. Naz API was machinery.

The highest-risk feature is not one exploit claim. It is the repository model. A searchable breach-data platform with buyer filters and API access can empower many smaller actors at once. That is infrastructure risk.

Dyslexic should be understood as a data-market infrastructure actor.

He is not merely selling random text files. He is building the middle layer between breach acquisition and buyer action:

• collect messy data;
• parse it;
• dedupe it;
• index it;
• filter it;
• expose it through bot/API interfaces;
• sell broad or targeted access;
• use free drops to pull users into the market;
• route higher-value buyers into private sales.

That middle layer is where harm multiplies. One stolen credential list is one incident. A searchable repository becomes a service. A service becomes an ecosystem. Naz API lived in that ecosystem.

Dyslexic is a high-risk breach-data operator and the creator of Naz API, a repository/search product that converted large credential and identity corpuses into usable marketplace inventory. His strongest demonstrated abilities are not glamorous exploit wizardry; they are data ingestion, parsing, packaging, filtering, and sales logistics.

The exploit and malware claims deserve attention, but they are secondary to the core threat. Naz API’s danger is scale and accessibility. It lowers the friction for other actors to find credentials, identify targets, enrich victims, and buy filtered data without doing the acquisition work themselves.

The cleanest one-line assessment:

Dyslexic is the breach-data plumber behind Naz API: messy, abrasive, technically uneven, but dangerous because he turned stolen data into searchable infrastructure.

• Naz Api Chat
• BlackForums Chat
• $CAMZ Chat
• Osint.pw Chat
• Breach data
• Combo lists
• Stealer logs
• OSINT tooling
• Criminal data brokers
• Credential repositories

Record created: 2026-06-12.