Taimoor_sial

When people talk about crypto projects they often focus on price charts partnerships or marketing announcements but the real story of whether a blockchain is alive or not is written somewhere else entirely it is written in the code repositories where developers spend thousands of hours building fixing breaking and rebuilding the system that will eventually carry real financial value Dusk is one of the few projects where this story is visible in a very concrete way because its development is not just happening in one place it is happening in two deep parallel tracks the core blockchain node and the zero-knowledge engine that makes its privacy and compliance model possible
On one side there is the dusk blockchain implementation itself written in Go This is the engine that handles peer to peer networking block production consensus data propagation and everything that turns a whitepaper into a running distributed system The activity you see in the dusk blockchain repository is not cosmetic It is the kind of work that only happens when a team is preparing a protocol for real world use You see constant changes to networking code consensus logic storage layers and performance tuning because a financial blockchain cannot afford to be fragile It has to run continuously under load survive network failures and behave predictably even when thousands of nodes are participating
At the same time there is a second equally important stream of development happening in the zerocaf repository Zerocaf is the protocol Dusk uses to build set inclusion proofs which are a critical part of how it achieves privacy and compliance at the same time This is the cryptographic heart of @Dusk It is what allows the network to prove that a transaction belongs to a valid set of approved participants or assets without revealing which specific one it is This is what makes things like private KYC private asset transfers and confidential smart contracts possible
Most blockchains either build a network first and bolt privacy on later or they build cryptography in isolation and struggle to integrate it into a live system Dusk is doing both at the same time The blockchain code and the zero knowledge code are evolving together This matters because privacy is not something you can just add to a finished network It has to be deeply woven into how data moves how blocks are built and how consensus works
The commit history shown in the image tells a very important story Over months you can see steady consistent work on the core blockchain code as well as bursts of intense activity on Zerocaf This is exactly what you expect from a team that is moving from research into deployment First the cryptography is refined Then it is integrated into the node software Then it is optimized Then it is tested under real conditions This back and forth is not something you can fake with marketing It shows that real engineers are solving real problems
What is especially important here is that Dusk is not just writing application level code It is writing foundational infrastructure Network code is some of the hardest software to build correctly You have to handle latency partitions malicious nodes and unpredictable conditions Cryptographic protocols are even harder because a tiny mistake can destroy security The fact that both of these layers are being actively worked on in parallel is a strong signal that Dusk is not chasing quick demos It is building something meant to last
This kind of development pattern is what you normally see in serious systems like operating systems or financial exchanges not in speculative crypto projects It means the team is spending its time making the system more robust more secure and more usable rather than just adding surface features to impress users
When you combine this with #dusk goals of supporting regulated finance confidential assets and zero knowledge identity the picture becomes even clearer You cannot build that kind of platform without deep sustained engineering effort The charts in the image are not just lines and bars They represent months of cryptographic research protocol design and low level system work
Inside $DUSK developer activity you can see a project that is quietly doing the hardest part of blockchain innovation turning advanced cryptography into something that actually runs at scale on a live network This is the kind of work that creates real long term value long after hype cycles have passed.

Seit Jahrzehnten wird die Identität im Internet auf die schlechteste mögliche Weise behandelt. Jedes Mal, wenn Sie ein Bankkonto eröffnen, sich bei einer Börse anmelden oder auf einen Finanzdienst zugreifen, werden Sie gebeten, Kopien Ihres Passes, Stromrechnungen und persönlicher Daten hochzuladen. Diese Dokumente werden dann in großen zentralen Datenbanken gespeichert, die im Laufe der Zeit gehackt, preisgegeben oder missbraucht werden. Sie verlieren die Kontrolle über Ihre eigene Identität, und Sie haben keine Ahnung, wer sie einsehen oder wo sie weitergegeben wird. Dieses fehlerhafte Modell ist einer der größten Gründe, warum Menschen dem digitalen Finanzwesen nicht vertrauen, obwohl alles andere bereits online gegangen ist.

Most blockchains tell a very simple story about how blocks are made. A block producer creates a block, validators vote on it, and if enough of them agree the block is added to the chain. On paper that sounds clean. In practice, it hides a huge amount of complexity and risk. When you can see who is producing blocks and who is voting, you also reveal who to attack, who to bribe, and who to pressure. Over time this visibility becomes the biggest weakness in the system. Dusk was designed around a different idea. It assumes that in real financial networks, the people who secure the system must be protected from being singled out. That is why its block agreement process is built on hidden committees and a two-step consensus model that balances speed, security, and privacy in a way that most blockchains never even attempt.
The process starts with the understanding that not every provisioner should be involved in every decision. @Dusk has a large pool of provisioners, which are participants who have staked DUSK and are eligible to help secure the network. But instead of having all of them vote on every block, which would be slow and easy to observe, Dusk uses cryptographic sortition to quietly select a small committee for each round. This selection happens locally and privately. Each provisioner runs a cryptographic lottery using their private key and public randomness from the chain. If they are selected, they learn it themselves. No one else knows. This means that before a block is even proposed, there is no public list of who is going to be responsible for deciding its fate. Attackers cannot prepare. Validators cannot coordinate. The system moves first and reveals itself later.
Once a committee has been selected, the first phase of block agreement begins, called block selection. During this phase, multiple candidate blocks may be proposed by different participants. These blocks contain transactions waiting to be included in the chain. The hidden committee receives these submissions and evaluates them according to the protocol’s rules. This is not a popularity contest. The committee is not choosing based on identity or reputation. It is choosing based on objective criteria like validity, fees, and ordering rules defined by the network. Each committee member independently checks the blocks and scores them. The block with the highest score becomes the candidate for the next stage. What matters here is that this process happens inside a small, hidden group, making it extremely difficult for anyone to manipulate or influence the outcome.
After a single candidate block has been selected, the protocol moves into the second phase, block reduction. This phase exists because even if the committee agrees on which block is best, the rest of the network still needs a cryptographic guarantee that this decision was not faked or manipulated. In block reduction, the committee members produce digital signatures on the selected block. These signatures are not tied to their public identities. Instead, they are bundled into a cryptographic proof that shows that enough valid committee members approved this exact block. This proof is small, efficient, and verifiable by anyone, even though the identities of the signers remain hidden. The two-step design is important because it separates choosing a block from proving that it was chosen correctly. This separation allows the network to move quickly while still being able to defend itself against fraud.
Once the block reduction proof has been created, the network enters the final phase, block agreement. This is where the selected block becomes part of the official chain. At this point, other parts of the network can see the block and the proof that it was approved by a valid committee. They do not need to know who voted. They only need to know that enough eligible provisioners did. The protocol provides immediate statistical finality, meaning that once a block is agreed upon, the probability of it being reversed becomes vanishingly small. This is crucial for financial applications, where settlement must be reliable and irreversible in practice. At the same time, the protocol includes protections against timeout and fork attacks, ensuring that even under adverse network conditions, the chain converges on a single history.
What makes this entire system powerful is not just that it reaches consensus, but how it does so. By hiding who is involved at every stage, #dusk removes many of the social and economic attack vectors that plague other proof-of-stake networks. There is no public validator leaderboard to corrupt. There are no known block producers to target. There are no predictable voting patterns to exploit. Every round is a fresh start with a new hidden committee, chosen by mathematics rather than politics. This makes attacks not just expensive but deeply uncertain. An attacker cannot even be sure they are attacking the right people.
This design is especially important for the kind of users Dusk is built for. Financial institutions, asset issuers, and regulated entities need a blockchain that can provide strong guarantees without exposing its internal workings to manipulation. They need a system that behaves more like a secure clearinghouse than a public chat room. Hidden committees and two-step consensus give Dusk that property. Decisions are made quickly by small groups, but verified globally by cryptography. Authority exists, but it is always temporary and always anonymous.
$DUSK block agreement mechanism reflects a deeper philosophy about how decentralized systems should work in high-stakes environments. True decentralization does not mean everyone shouts at once. It means power is widely distributed, constantly shifting, and impossible to pin down. By using hidden committees to select blocks and a two-step process to finalize them, Dusk creates a network that is both efficient and resilient. It can move at the speed of modern finance while still defending itself against the kinds of coordinated attacks that only become more dangerous as blockchains grow more valuable.

When people talk about database replication they often imagine entire tables being copied from one server to another. It sounds heavy slow and fragile. PostgreSQL chose a very different path. Instead of copying data it copies memory. That memory is the Write Ahead Log or WAL and the two processes that move this memory across machines are the WAL sender and the WAL receiver. Together they form the invisible nervous system that keeps a primary database and its standbys in perfect alignment.
Everything begins on the primary server where all writes happen. When a client inserts or updates data PostgreSQL does not immediately rush to update files on disk. It first writes a record of that change into WAL. This record is a precise description of what changed not the data itself but the actions that produced it. These WAL records are appended in strict order forming a timeline of the database’s life.
The $WAL sender is a background process that watches this timeline. As soon as new WAL records are written it reads them and streams them over the network to any connected standbys. It does not wait for checkpoints or table writes. It sends the database’s memory as it is being formed. This makes replication fast and continuous rather than periodic and bulky.
On the other side sits the WAL receiver on the standby server. Its job is to accept this stream and write it into the standby’s own WAL files. At this stage the standby has not yet applied the changes to its tables. It is simply building up the same memory the primary has. The standby is listening to the primary’s thoughts before they become physical reality on disk.
Another background process on the standby then reads these WAL files and replays them. It applies each change to the standby’s data pages in exactly the same order they happened on the primary. Insert this row. Update that value. Delete that record. By following the same log the standby reconstructs the same database state without ever being told what the state is. It learns by replaying history.
This design has deep consequences. Because the standby is driven by WAL it does not need special replication logic for every table or index. All data types all schemas and all operations are handled by the same universal mechanism. If it happened on the primary and it was logged it will happen on the standby.
It also means the standby can always catch up. If the network drops the WAL receiver simply reconnects and asks the sender for the WAL it missed. Because WAL is stored on disk on the primary the history is still there. Replication is resilient to interruptions because it is based on a durable log not on fragile snapshots.
The result is a system that feels almost alive. The primary thinks and the standbys listen. The sender speaks and the receiver remembers. Together they keep multiple machines sharing a single consistent reality even though they may be thousands of miles apart.
This is why PostgreSQL replication is so reliable. It is not copying data. It is sharing memory.
@Walrus 🦭/acc #walrus

Wenn jemand auf eine Schaltfläche in einer App klickt oder ein Formular auf einer Website absendet, wirkt es sofort. Eine Zahl ändert sich. Eine Aufzeichnung wird gespeichert. Eine Transaktion wird abgeschlossen. Doch hinter diesem Moment der Einfachheit verbirgt sich einer der sorgfältigsten Pipelines im Bereich der Informatik. In PostgreSQL heißt diese Pipeline Write-Ahead Logging, oder WAL, und sie verwandelt eine verletzliche Änderung im Arbeitsspeicher in eine dauerhafte, absturzsichere Wahrheit.
Die Reise beginnt, wenn ein Client eine Anfrage an PostgreSQL sendet. Es könnte sich um eine Aktualisierung, eine Einfügung oder eine Löschung handeln. PostgreSQL empfängt diese Anfrage und plant, wie sie angewendet werden soll. Zu diesem Zeitpunkt ist noch nichts Dauerhaftes geschehen. Die Änderung existiert nur als Absicht. Es ist eine Idee, keine Tatsache. Wenn das System jetzt abstürzen würde, würde die Datenbank so handeln, als hätte die Anfrage nie existiert. Das ist bewusst so. PostgreSQL lässt halbfertige Ideen nicht in sein Gedächtnis der Welt eindringen.

When people think about databases, they usually imagine tables, rows, and indexes quietly sitting on disk. They imagine that when a piece of data is written, it simply goes to its place and stays there. In reality, modern databases do not work like that at all. They are not just storing data, they are constantly remembering how the data became what it is. In PostgreSQL, that memory lives in a system called Write-Ahead Logging, or WAL. Without WAL, PostgreSQL would not just be slower or less reliable, it would be fundamentally unable to survive the messy, unpredictable world of real hardware.
To understand why $WAL is the memory layer of PostgreSQL, you have to start with the problem every database faces: computers crash. Power cuts happen. Disks fail. Processes are killed. If a database simply wrote data directly into tables and a crash happened halfway through, the file on disk could be left in a broken state. Some pages might reflect the new data, others might not. The database would wake up not knowing which version of reality is true. That kind of uncertainty is fatal for any system that claims to store truth.
WAL solves this by separating intent from storage. Before PostgreSQL changes anything in a table, it writes a description of that change into a sequential log. That log is the WAL. It is a running story of everything the database has ever tried to do. Insert this row. Update that value. Delete this record. Each action is written to WAL first, flushed to disk, and only then applied to the actual data pages. This is why it is called write-ahead logging: the database writes its memory before it writes its body.
This log becomes the authoritative history of the database. If a crash happens, PostgreSQL does not try to guess what was on disk. It replays its memory. On startup, it reads the WAL from the last known good checkpoint and applies every logged change again. Pages that were half written are corrected. Operations that were interrupted are finished or rolled back. The database reconstructs itself from its own past. WAL is not a backup. It is the database’s brain.
What makes this even more powerful is that WAL is not just for crash recovery. It is also the foundation of replication. In a replicated PostgreSQL setup, the primary server streams its WAL to one or more standby servers. Those standby servers do not receive table files or data blocks. They receive the same memory that the primary uses. They receive the story of every change. By replaying that story, they reconstruct the exact same database state.
This means a standby server does not need to be told what the database looks like. It learns by listening. It reads WAL records and applies them in order, just like the primary did. As long as it keeps receiving WAL, it stays in sync. If the primary crashes, the standby already has the full memory of what happened. It can step in and continue the story.
This is why WAL is more than a log. It is the continuity of the database. It is what allows PostgreSQL to survive time. Without it, every crash would be a kind of amnesia. With it, crashes become just pauses in a long narrative.
There is also a deeper elegance in how WAL works. The data pages on disk are not required to be consistent at every moment. They are allowed to be messy, because WAL exists. This allows PostgreSQL to optimize for performance. It can batch writes, reorder disk operations, and use memory aggressively, because the true state of the database is preserved in WAL. The log is small, fast, and sequential, which is exactly what disks are good at. The tables can be large and scattered, which is what disks are bad at. WAL turns that weakness into strength.
In a way, PostgreSQL’s tables are just a cache of its memory. The WAL is the truth. Tables are rebuilt from it after crashes. Standby servers rebuild themselves from it. Backups are validated against it. Even point-in-time recovery, where you rewind a database to an exact moment, works by replaying WAL up to a specific point. You are literally traveling through the database’s memory.
This idea is surprisingly close to how human memory works. We do not remember every detail of our lives in our bodies. We remember events. We remember changes. We remember stories. From those, we reconstruct who we are. PostgreSQL does the same. WAL is its autobiography. Tables are just the current snapshot.
Once you see WAL this way, you realize why it is the most important file in a PostgreSQL system. Lose the tables and you can rebuild them. Lose the WAL and you lose the past. Without the past, there is no future state that can be trusted.
This is also why modern distributed systems, blockchains, and storage networks increasingly look like they are built around logs rather than files. Memory is more powerful than state. WAL taught that lesson long before most people realized it.
PostgreSQL does not just store data. It remembers how data came to be. And WAL is where that memory lives.
@Walrus 🦭/acc #walrus