He couldn't bear the sight of stuffing or mashed potatoes gracing his ... Their dad, Ed, was the embodiment of the spirit of Thanksgiving, believing that the opportunity to have food on their table was something to be truly thankful for.

Why Travis Kelce Isn't A Fan Of Thanksgiving Food

Fall is a critical time for bears, and when people "attract bears with trash and food it can lead to very dangerous situations," an NPS official said.

People keep trying to feed and hold a bear on North Carolina's Blue Ridge Parkway and it's gotten so bad that the National Parks Service had to close parts of the road

Missoulians and others can donate food and cash until the Griz-Cat Brawl of the Wild game on Saturday, Nov. 18, in Missoula, when the food drive winner will be announced.

Can the Cats Food Drive kicks off Nov. 4

An 8-mile stretch of the Blue Ridge Parkway in North Carolina has been shuttered for the indefinite future, after visitors were spotted giving food to and trying to hold a juvenile bear. In a release cited by WXII,

8 Miles of Parkway Closed Due to People Bothering a Bear

"Bears are opportunistic just like many animals are," he said. He also said bears are learning to better adapt to their surroundings — and will stop at nothing to find food. FOX 35 News asked Baez what the proper protocol is for dealing with a wild animal.

Florida family living in fear as video shows bear burglars breaking into home ‘every other night’

Traps of any kind, leghold and body-gripping, should be outlawed in the interest of public safety if for no other reason.

Marc Cydan: A humane society reins in harmful practices, and embraces those that are beneficial

The National Park Service has closed an area in Asheville, North Carolina after multiple reports of visitors feeding and attempting to hold a young bear.

Park visitors try to feed, hold black bear: NPS

By Ellen Brabo, For Washington Daily News For most people, completing the entire Appalachian Trail is a monumental feat that requires months of dedication, rigorous planning, and unwavering determination.

From peaks to pavement: local coach completes Appalachian Trail

Any attempt made by a park visitor to come close enough to a bear to disturb or displace them is illegal and can result in fines or arrests, officials warned. Being too close can cause bears to become aggressive; park authorities advise visitors to back away slowly while keeping an eye on the bear.

National park partly shutters after visitors try to hold bear cub

The good news? It appears this widespread news is actually just an internet rumor. According to Matt McKinney from The Food Industry Association (FMI), there are no current restrictions in place for baking soda sales. “We reached out to a few sources ...

Do You Really Need ID To Buy Baking Soda?

Food Bear

foodbear

Eating entails more than fulfilling our nutritional needs, it goes deeper than that. It is communal, enhances friendships, and builds bonds. Chinua Achebe, an African literary icon, said: ” When people gather together to eat, it is not because they do not have food in their houses. They gather to eat because it is good for kinsmen to eat together. “ This underscores the deep impact food has in building relationships and society at large. With the advent of the metaverse, it is inevitable that food will find its way into web 3.0. Large brands have already started experimenting with this. This primer will look at how we can consume food in the metaverse and what food brands are currently doing as they get into web 3.0 FoodVerse:  What It Entails The Foodverse can be defined as bringing food into the metaverse. Basically, it is having an experience of eating, whether real or virtual food, while experiencing the metaverse. Firstly, brands can use the metaverse to enhance client experience when ordering food. People are used to getting menus when ordering meals. The metaverse can help project these menus in ‘real life’ and help a client have a visual experience of the food they would like to order, how it will look, and even feel it before they make an order. Secondly, friends can also meet together in a room and say have a drink or even share a meal while enjoying the immersive experience of the metaverse. For example, they can meet and have a meal while on a cruise ship, in the middle of a forest, or attend a live session of their favorite band. This digital immersion enhances their relationship and creates an experience that is similar to real-world events. Thirdly, food brands can use NFTs to create rare products, such as wine, and produce them in limited quantities. They can then mint NFTs for these products and sell them to their consumers. These NFTs are a mark of genuine ownership and enhance the brand value of their companies. Collectors would love this. Consumers have the option of selling these NFTs on secondary markets or even creating communities around them in the metaverse. Tokenizing food products also helps communities to feel ownership of the companies and their brands. Finally, users of Metaverse platforms such as Sandbox can also buy land, build their homes and invite friends over for meals. This will be a game changer as a user can invite multiple friends over from across the world and share virtual meals or drinks with them. Of course, there is still a long way to go before we bridge the divide between virtual and physical food especially when such diverse geographies are involved, but this would be a step in the right direction. Notable Brands in the Foodverse At the moment, virtual food may look like it has little value, however, in the long term, merging both the virtual and physical world is the direction to take. Companies are experimenting with instances where you order your food on the metaverse, get it delivered to you and you eat without necessarily leaving the experience. One notable brand doing this is Chipotle. They have partnered with Roblox to allow customers to have an experience of virtual food. When customers make orders on the metaverse, they earn credits which they can later redeem for free food delivered to their houses. This shows how the physical and digital worlds can merge. Other companies like Onerare are tokenizing the food experience by bringing food lovers and linking them up with celebrity chefs. They then create NFTs from the experience they get from preparing and sharing meals. All of this is done on the metaverse in a gamified manner. McDonald's is also planning to open restaurants in the metaverse. The company has already patented ten trademarks on the metaverse. It intends to leverage virtual experiences and enable customers to order food from the metaverse and get them delivered to their houses. Customers can also get digital food for their avatars. Other food companies that have filed for trademarks as they seek to launch products in the metaverse are Kraft Foods Group, In- N- Out Burger, and Del Monte Foods. Restaurants and food brands stand to benefit from getting into the Foodverse. Firstly, getting into the Foodverse helps these companies build customer relationships and engagement. Secondly, it also enhances brand awareness and can drive more consumers to their products. Finally, it offers an opportunity for restaurants to create multiple income streams.

Foodverse: Eating in the Metaverse

Homelessness and food insecurity are pressing issues across the United States, with cities grappling to provide adequate resources and support for those in need. Recent statistics reveal that New York City, for instance, faces a homelessness crisis unseen since the Great Depression, with over 350,000 individuals lacking stable housing. The current systems that tackle these issues often falter, suffering from inefficient resource distribution and outdated methods. AskBobby, an AI-driven solution created by Andrew Siah, specifically aims to change how social services are used. Unveiling the Crisis: The Struggle of Homelessness and Food Insecurity Addressing homelessness and food insecurity involves navigating a complex situation of resource allocation and real-time data management. Traditional approaches often struggle with inefficiencies, resulting in aid that doesn’t always reach those who need it most. For example, many social service organizations operate on limited budgets and outdated systems, leading to gaps in service delivery and difficulties in tracking aid effectiveness. In New York City alone, the number of homeless individuals has soared to approximately 350,000, a staggering 4.37% of the population. These figures highlight the need for more effective systems to manage and distribute resources. The existing solutions often need more agility and precision to respond to the dynamic needs of this vulnerable population. Andrew Siah’s Journey from Quantitative Developer to Social Impact Leader Andrew Wei Tung, a recent Columbia University graduate, has started to address homelessness and food insecurity, driven by a passion for leveraging technology to solve real-world problems using the latest technologies. Inspired by an AI Social Work Hackathon at Columbia Business School, Andrew began his mission to create AskBobby, an AI tool designed to enhance access to essential social services. Andrew has won several prestigious hackathons and co-founded New York AI Labs, a club for enthusiasts to research and build in public with AI. Winning these hackathons inspired him to work toward building projects that are beneficial to the public. How AskBobby Uses AI to Improve Social Work AskBobby is an AI-powered SMS tool designed to help connect individuals in need with available social services. The tool provides real-time assistance in locating essential resources such as food pantries, shelters, and healthcare facilities. Using a multilingual personalized language model, AskBobby ensures that users receive accurate and timely information tailored to their needs. One of AskBobby’s standout features is its ability to optimize resource allocation for non-profits. The tool helps these organizations manage their resources more efficiently, run surveys to gather feedback and improve their services based on real-time data. This optimization is crucial for non-profits, which often operate under tight constraints while trying to maximize their impact. Future Goals for AskBobby and the Growing AI Field Since its launch, AskBobby has significantly improved access to social services. It has been successfully developed with FoodbankNYC, a non-profit that has served 1.2 billion meals to date.  AskBobby has served hundreds of visitors and is expanding through its network. The tool’s effectiveness in streamlining access to essential resources demonstrates its potential to transform how social services are delivered. Andrew’s vision for AskBobby includes enhancing the tool’s capabilities and expanding its reach. Future developments aim to address homelessness and food insecurity by refining the AI’s ability to understand and respond to individual needs. Additionally, Andrew plans to collaborate with more non-profits to broaden the tool’s impact and explore new avenues for support. How AskBobby Is Setting a New Standard in Social Services AskBobby represents a significant advancement in technology and social services. Leveraging AI to provide real-time assistance and optimize resource allocation addresses critical social services challenges. Andrew Siah’s modern approach highlights the potential of AI to solve complex social issues while setting a precedent for future efforts in this field. AskBobby continues to grow and evolve as AI continues to do the same, and it promises to play a pivotal role in improving access to essential services and supporting the fight against homelessness and hunger. For tech professionals and social work experts, AskBobby is an example of how technology can drive meaningful change. Its success underscores the importance of integrating advanced tools and data-driven solutions into efforts to tackle social challenges. As the tool continues to develop, monitoring its progress and exploring opportunities for further innovation and collaboration will be crucial. Explore Opportunities to Collaborate With AskBobby and Drive Social Impact Many people are interested in becoming tech professionals or social workers. AskBobby is a great example of how to change the world using technology. AI gives real-time assistance in allocating the proper resources for each situation. Social workers have long been in need of more tools to help, and AskBobby is helping get this done. Those interested in learning more about AskBobby and its impact or exploring potential collaborations can visit the website or contact Andrew at andrew.siah@columbia.edu Homelessness and food insecurity are pressing issues across the United States, with cities grappling to provide adequate resources and support for those in need. Recent statistics reveal that New York City, for instance, faces a homelessness crisis unseen since the Great Depression, with over 350,000 individuals lacking stable housing. The current systems that tackle these issues often falter, suffering from inefficient resource distribution and outdated methods. AskBobby, an AI-driven solution created by Andrew Siah, specifically aims to change how social services are used. Unveiling the Crisis: The Struggle of Homelessness and Food Insecurity Addressing homelessness and food insecurity involves navigating a complex situation of resource allocation and real-time data management. Traditional approaches often struggle with inefficiencies, resulting in aid that doesn’t always reach those who need it most. For example, many social service organizations operate on limited budgets and outdated systems, leading to gaps in service delivery and difficulties in tracking aid effectiveness. In New York City alone, the number of homeless individuals has soared to approximately 350,000, a staggering 4.37% of the population. These figures highlight the need for more effective systems to manage and distribute resources. The existing solutions often need more agility and precision to respond to the dynamic needs of this vulnerable population. Andrew Siah’s Journey from Quantitative Developer to Social Impact Leader Andrew Wei Tung, a recent Columbia University graduate, has started to address homelessness and food insecurity, driven by a passion for leveraging technology to solve real-world problems using the latest technologies. Inspired by an AI Social Work Hackathon at Columbia Business School, Andrew began his mission to create AskBobby, an AI tool designed to enhance access to essential social services. Andrew has won several prestigious hackathons and co-founded New York AI Labs, a club for enthusiasts to research and build in public with AI. Winning these hackathons inspired him to work toward building projects that are beneficial to the public. How AskBobby Uses AI to Improve Social Work AskBobby is an AI-powered SMS tool designed to help connect individuals in need with available social services. The tool provides real-time assistance in locating essential resources such as food pantries, shelters, and healthcare facilities. Using a multilingual personalized language model, AskBobby ensures that users receive accurate and timely information tailored to their needs. One of AskBobby’s standout features is its ability to optimize resource allocation for non-profits. The tool helps these organizations manage their resources more efficiently, run surveys to gather feedback and improve their services based on real-time data. This optimization is crucial for non-profits, which often operate under tight constraints while trying to maximize their impact. Future Goals for AskBobby and the Growing AI Field Since its launch, AskBobby has significantly improved access to social services. It has been successfully developed with FoodbankNYC, a non-profit that has served 1.2 billion meals to date.  AskBobby has served hundreds of visitors and is expanding through its network. The tool’s effectiveness in streamlining access to essential resources demonstrates its potential to transform how social services are delivered. Andrew’s vision for AskBobby includes enhancing the tool’s capabilities and expanding its reach. Future developments aim to address homelessness and food insecurity by refining the AI’s ability to understand and respond to individual needs. Additionally, Andrew plans to collaborate with more non-profits to broaden the tool’s impact and explore new avenues for support. How AskBobby Is Setting a New Standard in Social Services AskBobby represents a significant advancement in technology and social services. Leveraging AI to provide real-time assistance and optimize resource allocation addresses critical social services challenges. Andrew Siah’s modern approach highlights the potential of AI to solve complex social issues while setting a precedent for future efforts in this field. AskBobby continues to grow and evolve as AI continues to do the same, and it promises to play a pivotal role in improving access to essential services and supporting the fight against homelessness and hunger. For tech professionals and social work experts, AskBobby is an example of how technology can drive meaningful change. Its success underscores the importance of integrating advanced tools and data-driven solutions into efforts to tackle social challenges. As the tool continues to develop, monitoring its progress and exploring opportunities for further innovation and collaboration will be crucial. Explore Opportunities to Collaborate With AskBobby and Drive Social Impact Many people are interested in becoming tech professionals or social workers. AskBobby is a great example of how to change the world using technology. AI gives real-time assistance in allocating the proper resources for each situation. Social workers have long been in need of more tools to help, and AskBobby is helping get this done. Those interested in learning more about AskBobby and its impact or exploring potential collaborations can visit the website or contact Andrew at andrew.siah@columbia.edu website website

AskBobby: The AI Tool Helping People in Need Locate Social Services

AskBobby: la herramienta de inteligencia artificial que ayuda a las personas necesitadas a encontrar servicios sociales

AskBobby: a ferramenta de intelixencia artificial que axuda ás persoas necesitadas a localizar os servizos sociais

AskBobby: כלי הבינה המלאכותית שעוזר לאנשים במצוקה לאתר שירותים חברתיים

AskBobby. AI գործիք, որն օգնում է կարիքավոր մարդկանց գտնել սոցիալական ծառայություններ

AskBobby: Alat AI yang Membantu Orang yang Membutuhkan Menemukan Layanan Sosial

AskBobby: 困っている人が社会福祉サービスを探すのを助ける AI ツール

AskBobby: AI ინსტრუმენტი, რომელიც ეხმარება გაჭირვებულ ადამიანებს სოციალური სერვისების პოვნაში

AskBobby: 도움이 필요한 사람들이 사회 서비스를 찾을 수 있도록 돕는 AI 도구

AskBobby: Esaleli ya AI oyo ezali kosalisa bato oyo bazali na mposa ya koluka ba services socials

AskBobby: A ferramenta de IA que ajuda pessoas necessitadas a localizar serviços sociais

BazaBobby: Igikoresho cya AI gifasha abantu bakeneye kubona serivisi zimibereho

AskBobby: เครื่องมือ AI ที่ช่วยให้ผู้คนที่ต้องการความช่วยเหลือค้นหาบริการทางสังคม

5 Real-Life Uses of Blockchain and AI That'll Make You a Web3 Believer

5 skutečných využití blockchainu a umělé inteligence, které z vás udělají věřícího Web3

5 Real-Life Anvendelser af Blockchain og AI, der vil gøre dig til en Web3-troende

5 usos reales de blockchain e inteligencia artificial que te convertirán en un creyente de la Web3

5 کاربرد واقعی بلاک چین و هوش مصنوعی که شما را به یک باورمند Web3 تبدیل می کند

ब्लॉकचेन और AI के 5 वास्तविक उपयोग जो आपको वेब3 का समर्थक बना देंगे

ブロックチェーンと AI の 5 つの実用例で Web3 信者になる

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Sizni Web3 ga ishonuvchiga aylantiradigan Blockchain va AI-dan 5 ta haqiqiy foydalanish

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The ideology serves as a powerful mechanism not only for uniting individuals with similar interests but also for exchanging experiences and ideas, embodying the principle that sharing is indeed caring. is a great example of how a community of enthusiasts can change the world. Its are highly impressive and we can claim that most of the Internet works on Linux’s horsepower. open source Linux usage statistics Bad programmers worry about the code. Good programmers worry about data structures and their relationships. Linus Torvalds Managing an open-source project carries significant weight, particularly as its popularity grows: it brings forth the spotlight on technical expertise and architectural choices. Popular projects could be a treasure trove of knowledge for anyone who wants to boost their skills. Let's delve deeply into a few notable projects. Why focus on open source? First of all, it embodies top-tier coding standards, integrating best practices championed by industry leaders. My firm belief lies in the notion that genuine programmers are fueled by passion and curiosity. Why not learn something new and integrate it into our daily practice? Secondly, hacking code could take your skills to a whole new level if you are consistent and tenacious. Last but not least, it could help you jump right into the project you like: find new buddies, brand yourself as a skilled developer, and get the job of your dreams. Help the community to solve their problems, and the reward will not keep you waiting. Take your cup of coffee, let’s get straight to work. Linux Kernel The very heart of the Linux system — its kernel. Indeed, this is not merely a singular project, but rather a constellation of diverse projects unified by a central idea: tying up different parts of hardware and providing a sustainable, secure API for the end users. The Linux codebase is huge and contains a lot of vendor-specific code, but it is well-organized and structured. I strongly recommend everyone interested to read . It might be a bit outdated but it will foster a solid understanding of how the kernel internals work. The kernel version I have on the table is 6.3. this Radix tree for memory management At the very beginning of the main.c we can spot definitions of some functions like . It is responsible for initializing the data structure that is used in memory management processes. radix_tree_init In the codebase, the radix_tree_root struct is an alias for a wrapper above the struct. It a lock (pthread mutex); xa_flags — the enum that tells how memory allocations will be performed; and the pointer to xa_head. XArray holds gfp_t struct xarray {
	spinlock_t	xa_lock;
/* private: The rest of the data structure is not to be used directly. */
	gfp_t		xa_flags;
	void __rcu *	xa_head;
}; typedef enum {
	GFP_KERNEL,
	GFP_ATOMIC,
	__GFP_HIGHMEM,
	__GFP_HIGH
} gfp_t; If all of the entries in the array are NULL, xa_head is a NULL pointer. If the only non-NULL entry in the array is at index 0, xa_head is that entry. If any other entry in the array is non-NULL, xa_head points to a xa_node — the internal struct that handles offset, counts, slots, and other fields. struct xa_node {
	unsigned char	shift;		/* Bits remaining in each slot */
	unsigned char	offset;		/* Slot offset in parent */
	unsigned char	count;		/* Total entry count */
	unsigned char	nr_values;	/* Value entry count */
	struct xa_node __rcu *parent;	/* NULL at top of tree */
	struct xarray	*array;		/* The array we belong to */
	union {
		struct list_head private_list;	/* For tree user */
		struct rcu_head	rcu_head;	/* Used when freeing node */
	};
	void __rcu	*slots[XA_CHUNK_SIZE];
	union {
		unsigned long	tags[XA_MAX_MARKS][XA_MARK_LONGS];
		unsigned long	marks[XA_MAX_MARKS][XA_MARK_LONGS];
	};
}; The radix tree data structure contains “nodes” that link with each other based on the int key (index). The node contains several slots, each of which can handle a pointer. The main idea here is to keep a tree wide, not deep — it is effective for fast addressing. When the function is called by a user (it can be a device driver), the following actions occur: radix_tree_insert __radix_create_tree creates a slot in the tree if it is necessary, based on the passed index which is used to check the size of the tree. Or raise an error if there is no available memory; insert_entities pass an item to the slot; some asserts check that offsets are applied correctly to the data structure. Wherever a user of the tree is going to look up a specific page, they need to look up it with an index. The lookup phase is quite efficient because of the broad nature of the structure. It is based on the key that is modified on the user side. Take a look at the for more insight into how it works. article Users of this data structure include GPU and network device drivers, which maintain memory page pointers in a structured format rather than juggling them. It's like having a pocket dimension! This is where Linus's earlier quote comes into play. Consider the essence of the data you handle; aim for efficiency, compactness, and cleanliness in its usage, regardless of the language or project you're involved in. In 2021 Liam Howlett (no, another Liam) and Matthew Wilcox introduced data structure. I encourage you to read more about the intentions, motivation, and realization details and . the maple tree here here Kubernetes Let's direct our steps to the point of the architectural design. One of the most famous orchestrators, invented by Google and spread around the world at rocket pace — Kubernetes. There are a lot of fairly new resources out there covering the internal details, for example here is a that you can start exploring as you delve deeper into the details. repository The whole point of this article is to get some useful insights that answer not just “how” but “why”. Understanding the motivation is crucial, in my opinion, that is how we can “catch a mindset” to re-implement all those cool staff in our daily job. Let’s briefly recall what the Kubernetes is. This is the system that manages containers within the applications and provides orchestration features such as scaling and high availability. Kubernetes’ main components are the API service, the scheduler, etcd — the key-value storage, the controller service, and the agent service — “kubelete” on each node to manage containers. Guys from developed it to utilize the compute resources of their cloud . Their main intention was a “great container management system”. Google as efficiently as possible Architectural highlights The very first thing I’d like to highlight is the distributed nature of execution. To synchronize all of the user’s entities states the orchestrator uses etcd — the key-value storage which information in a tree data structure. It is a common pattern that presents a to manage different states between multiple entities, with one slight difference: the storage is also used to “instruct” all of the components about the final cluster’s state. keeps So the whole system uses the storage as a source of truth to keep maintaining the desired state of a cluster. source of truth One more nice architectural decision is the — container runtime interface. CRI It allows Kubernetes to work not just with Docker but many others (CRI-O, Dockerd) by defining the , just like one from RFC. The kubelet agent utilizes GRPC to connect with a runtime, so in case one would like to implement its own runtime, they should implement the exact interface on their side. The concept aims to loosen the connection between the kubelet agent and a runtime which manages containers. interaction protocol At the end let’s briefly look at the “sidecar” pattern that was presented by third-party services like and has recently been within the core Kubernetes functionality. Istio implemented The whole idea is to embed a service to the main application that helps to handle some specific cases such as proxy or load-balancing. Kubernetes manages this container as a whole different entity of a pod: the container can the entire pod status; restarting and updating separately of the main application; resources with the main app. influence share The main point here is that the main application may know nothing about the sidecar, while the behavior of the entire service can be changed. Bless Open Source, we how new functionality was implemented by the community. can trace PostgreSQL PostgreSQL is not just a database but a whole platform. After the client connects to the server a new is established. The query goes through many while executed: parsing, the rewriting system, planning and executing. backend process stages The PSQL server has a bunch of that allow developers to interact with different internal components. For example, we can our scan provider aimed, for some optimizations at each stage. Or create an . Even though the overall process required for specific knowledge and the documentation could be a bit knotty, interfaces write index add-on the loosely coupled architecture empowers developers to vastly expand functionality. There are many that we can use within the platform and the “bloom index” is one of them. extensions realization Bloom index I faced a problem of growing indexes back in the day. The task was to implement the custom search filter: every column in the database and the combination of it could be a key for a search. Building indexes for each and every column could be a bad decision because of the complexity and size. For example, if we are sure that item==2 is False there is no reason to check all other fields. I remembered the — the probabilistic data structure that could return a false-positive result (because of hash collision) and be quite sure when the data is absent. As I’ve mentioned earlier PSQL has an extension for this data structure. The very first idea that comes to mind is to reduce the dimension of possible variations removing the query values that we are sure are not in the database. Bloom filter Bloom filter in a nutshell — it’s just a bit array. Every item would be hashed with a certain amount of different functions that translates it to some index. That index will set the exact bit in the filter’s array. In the case of 3 different functions, an item would set 3 different bits. At the lookup phase, the query would be also hashed and compared within the bits in the array. If at least one of the bits is not set, we can be sure the item is absent. If all of them were set, it is still possible that the item was not placed — that is because each bit we have found could refer to other items — the collision takes place. Check out this great of how it works within the examples. explanation To start using the bloom index within the PSQL we need to create it properly: CREATE INDEX bloom_idx_bar ON foo.bar USING bloom (id,dept_id,zipcode) WITH (length=80, col1=4, col2=2, col3=4); where “length” — is the length of the array of bits for each row; and the “col” numbers — describe how many bits would be occupied by the column (how many functions would be applied). By increasing these numbers we can reduce false-positive shots. Of index creation in PSQL is a bit complicated, we should understand internal data structures and protocols; let’s try to make the observation short and sweet. The function promotes the data structure’s functions as an IndexAmRoutine pointer — a pointer to the , that represents an API for the index access methods. blhandler struct The function creates the new index: allocate memory pages, scan the table for tuples that will be indexed and in the structure. blbuild BloomState typedef struct BloomState
{
	FmgrInfo	hashFn[INDEX_MAX_KEYS];
	Oid			collations[INDEX_MAX_KEYS];
	BloomOptions opts;			/* copy of options on index's metapage */
	int32		nColumns;

	/*
	 * sizeOfBloomTuple is index-specific, and it depends on reloptions, so
	 * precompute it
	 */
	Size		sizeOfBloomTuple;
} BloomState; When we insert data to the index, we pass it in the form of an array within the array of booleans that marks NULL values to skip it at the indexing phase. The BloomFormTuple function forms the BloomTuple struct from the data — this is the part where the data would be signed. The tuple would be passed to a memory page after that phase. BloomTuple *
BloomFormTuple(BloomState *state, ItemPointer iptr, Datum *values, bool *isnull)
{
	int			i;
	BloomTuple *res = (BloomTuple *) palloc0(state->sizeOfBloomTuple);

	res->heapPtr = *iptr;

	/* Blooming each column */
	for (i = 0; i < state->nColumns; i++)
	{
		/* skip nulls */
		if (isnull[i])
			continue;

		signValue(state, res->sign, values[i], i);
	}

	return res;
} At the scan phase the forms the struct which contains the index’s data and the makes the TIDBitmap struct from it — the internal data structure that holds item pointers — reading BloomState page by page. blbeginscan IndexScanDesc blgetbitmap I hope the explanation isn't too confusing, PSQL internals are quite specific. Bloom filter is a pretty common data structure, PSQL uses the same approach for some purposes. It is also broadly used in many different situations: from cache-hitting cases to implementations. internal kernel packet filter More to know In the article, I’ve just lifted the veil of the shiny open-source world. There are many more great implementations you could meet in the wild: and — if you’d like to catch the magic of the “fair” resource allocation; — to understand the simultaneous editing in Google Docs. CFS CFQ operational transform and CRDT The whole software world is in motion: sidecars and operators to be the new model for software delivery; eBPF is in a to change the service-mesh post. claimed hurry Take a look at the elegance of the great things’ internals.

Algorithms in the Wild: Reusable Insights and Strategies from the Open Source Community

Pydantic is a popular open-source Python library for data validation and modeling. It offers tools to define the structure and rules of your data, ensuring its consistency and reliability. Pydantic is looking to have a lot of potential in AI, in regards to data preprocessing and cleaning. Its ability to validate and serialize data makes it an ideal choice for handling the large and complex datasets often used in AI applications. Additionally, Pydantic’s support for type annotations and type checking can help catch errors early in the development process, making it easier to build and maintain reliable AI systems. Not just that but Pydantic’s integration with popular AI libraries such as TensorFlow and PyTorch, allows for seamless data manipulation and model training. Why Use Pydantic Data Validation Pydantic enforces data types and constraints you define, catching invalid entries before they cause issues. This is crucial in AI, where incorrect data can lead to biased or inaccurate models. Data validation is a process that ensures the data entered into a system is correct and useful. It checks the accuracy and quality of data before it’s processed. Here are a few examples of data validation using the Pydantic library in Python: : Pydantic uses Python-type hints to validate data. For instance, in the following code, the class has attributes , , , and with specific type hints. Pydantic validates the data against these type hints. Type Hints Validation Fruit name color weight bazam If the data doesn’t match the type hints, a validation error is raised. from typing import Annotated, Dict, List, Literal, Tuple
from pydantic import BaseModel

class Fruit(BaseModel):
    name: str
    color: Literal['red', 'green']
    weight: Annotated[float, Gt(0)]
    bazam: Dict[str, List[Tuple[int, bool, float]]]

print( 
    Fruit(
        name='Apple', 
        color='red', 
        weight=4.2, 
        bazam={'foobar': [(1, True, 0.1)]}
    )
) : Pydantic also has a strict mode where types are not coerced, and a validation error is raised unless the input data exactly matches the schema or type hint. Here’s an example: Strict Mode Validation from datetime import datetime
from pydantic import BaseModel, ValidationError

class Meeting(BaseModel):
    when: datetime
    where: bytes

try:
    m = Meeting.model_validate(
        {'when': '2020-01-01T12:00', 'where': 'home'}, 
        strict=True
    )
except ValidationError as e:
    print(e) : Pydantic allows for customizing validation via functional validators. For instance, in the following code, a custom validator is used to check if the attribute is ‘now’ and if so, it returns the current datetime. Custom Validators when from datetime import datetime, timezone
from pydantic import BaseModel, field_validator

class Meeting(BaseModel):
    when: datetime

    @field_validator('when', mode='wrap')
    def when_now(cls, input_value, handler):
        if input_value == 'now':
            return datetime.now()
        when = handler(input_value)
        if when.tzinfo is None:
            when = when.replace(tzinfo=timezone.utc)
        return when These examples demonstrate how Pydantic can be used for data validation in Python, ensuring that the data being processed matches the expected types and constraints Data Modeling Define the structure of your data, including nested objects and relationships. This makes it easier to work with complex data sets and helps keep your code organized. Serialization/Deserialization Convert data between different formats like JSON, Python dictionaries, and others. This allows seamless integration with external APIs and data sources. How Is Pydantic Useful in AI? One of the burgeoning challenges in the realm of artificial intelligence (AI), particularly when working with Large Language Models (LLMs), is structuring responses. These sophisticated models can generate vast quantities of unstructured data, which then necessitates meticulous organization. This is where Pydantic, a data validation and settings management library in Python, steps in with an elegant solution. It simplifies the formidable task by enabling developers to define a clear model for their data, ensuring that the responses from LLMs are well-structured and conform to expected formats. Guaranteed structure output with Ollama and Pydantic. pic.twitter.com/YF8cFAsaap — jason (@jxnlco) February 8, 2024 Leveraging Models to Structure Large Language Model Responses When interfacing with LLMs, it’s crucial to not just receive data but to parse and utilize it effectively. Pydantic facilitates this by allowing the creation of models that serve as blueprints for the expected data. This means that developers can predefine the structure, types, and requirements of the data they are to handle, making it easier to manage and ensuring that the information is in the correct form for further processing or analysis. Pydantic 2.7: Optional Support for Incomplete JSON Parsing The upcoming Pydantic version 2.7 introduces optional support for parsing and validating incomplete JSON, which is particularly beneficial for AI applications. This feature aligns perfectly with the needs of developers processing streamed responses from an LLM. Instead of waiting for the entire payload, developers can start processing the data as it arrives, enabling real-time data handling and reducing latency in the AI system’s response. Integration With DSPy and JSON Schemas Furthermore, there is ongoing experimentation with combining , Pydantic types, and JSON Schemas to further enhance data validation and transformation capabilities. Such integrations broaden the potential applications of Pydantic in the AI space by leveraging the advantages of each tool, leading to more robust and versatile data-handling solutions. DSPy OpenAI Function Calls and Query Plans An often-underappreciated aspect of OpenAI’s capabilities is its function-calling feature that permits the generation of entire query plans. These plans can be represented by nested Pydantic objects, adding a structured and executable layer over retrieval and Reading Comprehension Answer Generator (RAG) pipelines. By adopting this method, developers can obtain plan-and-execute capabilities that allow for handling intricate queries over assorted data sources. An example of this in practice is , which capitalizes on such a layered approach to accessing and for generating structured data. LlamaIndex Also published here

Pydantic: What It Is and Why It's Useful

The Open Worldwide Application Security Project is an online community that produces freely available articles, methodologies, documentation, tools, and technologies in the fields of IoT, system software and web application security. The OWASP provides free and open resources. It is led by a non-profit called The OWASP Foundation. The OWASP Top 10 - 2021 is the published result of recent research based on comprehensive data compiled from over 40 partner organizations. -- OWASP website The OWASP regularly publishes a Top 10 vulnerability report. The report targets vulnerabilities in web applications. In this post, I'd like to describe how to fix some of them via the . Apache APISIX API Gateway The OWASP Top 10 2021 In 2021, the report mentions: A01:2021-Broken Access Control A02:2021-Cryptographic Failures A03:2021-Injection A04:2021-Insecure A05:2021-Security Misconfiguration A06:2021-Vulnerable and Outdated Components A07:2021-Identification and Authentication Failures A08:2021-Software and Data Integrity Failures A09:2021-Security Logging and Monitoring Failures A10:2021-Server-Side Request Forgery For more details, please check the complete . report Fixing a vulnerability depends on its exact nature. For example, fixing is process-driven, requiring discipline in managing versions and retiring older ones. Some, however, are technical and only require proper configuration in the reverse proxy or API Gateway, , . Vulnerable and Outdated Components e.g. Server Side Request Forgery Nobody cares about security Security is a touchy subject because hardening security doesn't bring any value to the business. Career-driven managers won't care about security as they won't be able to showcase they increased the company's profit by X% on their next yearly evaluation. Unless the board considers security seriously, chances are nobody will care. For this reason, most organizations implement checkbox-based security, aka plausible deniability. If you're interested in implementing security properly, I've written some thoughts in a previous blog post: . Treat security as a risk All in all, securing applications will not get a lot of budget, if any. Hence, we must be smart about it and search for an existing component. Fortunately, the OWASP offers an out-of-the-box configuration to handle the Top 10, which is fixable via a configuration named . Unfortunately, it targets ModSecurity: Core Rule Set ModSecurity, sometimes called Modsec, is an open-source web application firewall (WAF). Originally designed as a module for the Apache HTTP Server, it has evolved to provide an array of Hypertext Transfer Protocol request and response filtering capabilities along with other security features across a number of different platforms including Apache HTTP Server, Microsoft IIS and Nginx. It is free software released under the Apache license 2.0. -- ModSecurity on Wikipedia While it's theoretically possible to configure Nnginx via Apache APISIX configuration, there's another more straightforward way. The OWASP Core Ruleset and Coraza The description of the Core Ruleset is pretty relevant to our needs: The OWASP® ModSecurity Core Rule Set (CRS) is a set of generic attack detection rules for use with ModSecurity or compatible web application firewalls. The CRS aims to protect web applications from a wide range of attacks, including the OWASP Top Ten, with a minimum of false alerts. The CRS provides protection against many common attack categories, including: SQL Injection (SQLi) Cross Site Scripting (XSS) Local File Inclusion (LFI) Remote File Inclusion (RFI) PHP Code Injection Java Code Injection HTTPoxy Shellshock Unix/Windows Shell Injection Session Fixation Scripting/Scanner/Bot Detection Metadata/Error Leakages -- OWASP® ModSecurity Core Rule Set website OWASP also provides , a port of ModSecurity available as a Go library. is built on top of Coraza and implements the , which specifies a set of Wasm interfaces for proxies. Finally, Apache APISIX offers proxy-wasm integration. Coraza Coraza Proxy Wasm proxy-wasm ABI Putting it all together Let's sum up: The OWASP provides a list of the Top 10 web security vulnerabilities It implements them for ModSecurity via the Core Ruleset Coraza is a port of ModSecurity, available as a proxy-wasm implementation We can configure Apache APISIX with sane and secure defaults this way. Let's do it. First things first: Coraza isn't part of the Apache APISIX distribution. Yet, it's straightforward to add it here with Docker: FROM apache/apisix:3.8.0-debian

ENV VERSION 0.5.0                                                           #1
ENV CORAZA_FILENAME coraza-proxy-wasm-${VERSION}.zip                        #1

ADD https://github.com/corazawaf/coraza-proxy-wasm/releases/download/$VERSION/$CORAZA_FILENAME . #2

USER root                                                                   #3

RUN <<EOF

  apt-get install zip -y                                                    #4
  unzip $CORAZA_FILENAME -d /usr/local/apisix/proxywasm
  rm $CORAZA_FILENAME
  apt-get remove zip -y
  chown -R apisix:apisix /usr/local/apisix/proxywasm

EOF

USER apisix                                                                 #5 Define variables for better maintainability Get the Coraza Wasm release In recent APISIX versions, the user is to harden security. As we need to install packages, we must switch to . apisix root Install as it's not installed, unzip the downloaded archive, remove the archive, uninstall , and change the owner of the extracted folder unzip unzip Switch back to user apisix The next step is configuring APISIX itself to use the Coraza Wasm plugin. wasm:
  plugins:
    - name: coraza-filter                                                   #1
      priority: 7999                                                        #2
      file: /usr/local/apisix/proxywasm/coraza-proxy-wasm.wasm              #3 Filter's name set in Wasm code Set the highest priority so it runs before any other plugin Path to the extracted file, see the above Dockerfile Finally, we can assign the plugin to routes or set it as a global rule to apply to every route. I'm using static configuration: global_rules:
  - id: 1
    plugins:
      coraza-filter:                                                        #1
        conf:
          directives_map:                                                   #2
            default:
              - SecDebugLogLevel 9                                          #3
              - SecRuleEngine On                                            #4
              - Include @crs-setup-conf                                     #5
              - Include @owasp_crs/*.conf                                   #6
          default_directives: default                                       #7 Configure the plugin now that it's available coraza-filter Define configurations. Here, we define a single one, , but we could define several and use different ones in different routes default Increase the log level to see what happens in logs Switch on the engine Use Coraza setup Use all rules. We could pick and choose the ones we want for more fine-grained control Use the configuration defined above default We proceed to define routes to to test our setup. Let's call the route to : https://httpbin.org/ /get curl localhost:9080?user=foobar The response is as expected: {
  "args": {
    "user": "foobar"
  }, 
  "headers": {
    "Accept": "*/*", 
    "Host": "localhost", 
    "User-Agent": "curl/8.4.0", 
    "X-Amzn-Trace-Id": "Root=1-65b9fa13-75900dc029e156ec764ae204", 
    "X-Forwarded-Host": "localhost"
  }, 
  "origin": "192.168.65.1, 176.153.7.175", 
  "url": "http://localhost/get?user=foobar"
} Now, let's try to send JavaScript in the query string. There's no way this request is expected server-side, so our infrastructure should protect us from it. curl 'localhost:9080?user=<script>alert(1)</script>' The response is a 403 HTTP status code. If we look at the log, we can see the following hints: Coraza: Warning. XSS Attack Detected via libinjection [file "@owasp_crs/REQUEST-941-APPLICATION-ATTACK-XSS.conf"]
Coraza: Warning. NoScript XSS InjectionChecker: HTML Injection
Coraza: Warning. Javascript method detected
Coraza: Access denied (phase 1). Inbound Anomaly Score Exceeded in phase 1 Coraza did the job! Conclusion Most organizations don't incentivize for security. Hence, we need to be smart about it and use existing components as much as possible. We can harden Apache APISIX against the OWASP Top 10 by using Coraza and the Core Ruleset. To go further: OWASP Security Top 10 OWASP ModSecurity Core Ruleset OWASP Coraza WAF APISIX - Integrate with Coraza The complete source code for this post can be found on . GitHub Originally published at on February 4th, 2024 A Java Geek

How to Address Web Vulnerabilities With Apache APISIX API Gateway

Apache APISIX API গেটওয়ে দিয়ে কীভাবে ওয়েব দুর্বলতাগুলি মোকাবেলা করবেন

So beheben Sie Web-Schwachstellen mit dem Apache APISIX API Gateway

Cómo abordar las vulnerabilidades web con Apache APISIX API Gateway

Comment remédier aux vulnérabilités Web avec Apache APISIX API Gateway

अपाचे एपीआईएसआईएक्स एपीआई गेटवे के साथ वेब कमजोरियों को कैसे संबोधित करें

Apache APISIX API ゲートウェイを使用して Web の脆弱性に対処する方法

Apache APISIX API 게이트웨이를 사용하여 웹 취약점을 해결하는 방법

Como resolver vulnerabilidades da Web com Apache APISIX API Gateway

Как устранить веб-уязвимости с помощью API-шлюза Apache APISIX

Apache APISIX API Ağ Geçidi ile Web Güvenlik Açıkları Nasıl Giderilir?

Cách giải quyết các lỗ hổng web bằng cổng API APISIX của Apache

如何使用 Apache APISIX API 网关解决 Web 漏洞

Introduction Frontend developers often face a problem related to application architecture. It requires the use of an architecture that can easily scale and provide loose coupling and high cohesion between application modules. This article discusses the Feature-Sliced Design architecture, as, in my opinion, it is the best among the available options. It also explores the idea of FSD and the problems this architectural methodology solves. We will compare FSD with classical and modular architectures and examine their pros and cons. First and foremost, let's distinguish three concepts: layer, slice, and segment. Layers Layers are top-level directories and the first level of application decomposition. They are limited in number - a maximum of 7 layers - and standardized, although some of them are optional. Currently, the following layers are distinguished: Each layer has its own zone of responsibility and is business-oriented. Let's consider each layer separately. app: This is where the application logic is initialized. Providers, routers, global styles, global type declarations, etc. are defined here. It serves as the entry point for the application. processes: This layer handles processes that span across multiple pages, such as multistep registration. This layer is considered deprecated but can still be occasionally encountered. It is an optional layer. pages: This layer includes the application's pages. widgets: These are standalone UI components used on pages. features: This layer deals with user scenarios and functionality that carries business value. For example, likes, writing reviews, rating products, etc. It is an optional layer. entities: This layer represents business entities. These entities can include users, reviews, comments, etc. It is an optional layer. shared: This layer contains reusable components and utilities that are not tied to specific business logic. It includes a UI kit, Axios configuration, application configuration, helpers that are not bound to business logic, etc. These layers help organize the codebase and promote a modular, maintainable, and scalable architecture. One of the key features of Feature-Sliced Design is its hierarchical structure. In this structure, entities cannot use functionality from features because features are higher in the hierarchy. Similarly, features cannot use components from widgets or processes, as the layers above can only utilize the layers below. This is done to maintain a linear flow that is directed only in one direction. The lower a layer is positioned in the hierarchy, the riskier it is to make changes to it since it is likely to be used in more places in the code. For example, the UI kit in the shared layer is used in the features, widgets, and even page layers. Slices In each of the layers, there are subdirectories - slices—the second level of application decomposition. In slices, the connection is not to abstract things but to specific business entities. The main goal of slices is to group code by its value. Slice names are not standardized, as they are directly determined by the business area of the project. For example, in a photo gallery, there may be sections like photo, album, and gallery. A social network would require slices like posts, users, and newsfeeds. Closely related fragments can be structurally grouped in a directory, but they must adhere to the same isolation rules as other slices - there should be no shared access to the code in this directory. Segments Each slice consists of segments. Segments help to divide the code within a slice based on its purpose. Depending on the team's agreements, segments can change in composition and naming. The following segments are more commonly used: api - necessary server requests. UI - UI components of the slice. model - Business logic, i.e., interaction with the state. For example, actions and selectors. lib - auxiliary functionality used within the slice. config - necessary configuration of the slice, but the config segment is rarely encountered. consts - necessary constants. Public API Each slice and segment has a Public API. The Public API is represented by an index.js or index.ts file, which allows extracting only the necessary functionality from the slice or segment to the outside and isolating unnecessary functionality. The index file serves as an entry point. Rules for the Public API: Application slices and segments use only the functionality and components of the slice that are defined in the Public API index file. The internal part of the slice or segment that is not defined in the Public API is considered isolated and only open for access within the slice or segment itself. The Public API simplifies working with import and export, so when making changes to the application, there is no need to change imports everywhere in the code. Deeper Into Architecture Abstraction and Business Logic The higher the layer, the more it is tied to the specific business node and the more business logic it contains. The lower the layer, the more abstractions, reusability, and lack of autonomy in the layer. How Does FSD Solve the Problem? One of the tasks of Feature-Sliced Design is to achieve loose coupling and high cohesion. It is important to understand how FSD achieves this result. In OOP, these problems have long been solved through concepts such as , , , and . These concepts ensure isolation, reusability, and versatility of code, where different results are obtained depending on how a component or functionality is used. polymorphism encapsulation inheritance abstraction Feature-Sliced Design helps apply these principles in the frontend. and are achieved through layers. Since the lower layers are abstract, they can be reused in higher layers, and depending on the conditions, a component or functionality can work differently based on the specified parameters or props. Abstraction polymorphism is achieved through the Public API, which isolates what is not needed from the outside in slices and segments. Access to the inner segments of a slice is restricted, and the Public API is the only way to access functionality and components from a slice or segment. Encapsulation is also achieved through layers, as higher layers can reuse lower layers. Inheritance Comparison With the Classic Architecture I believe you have come across classic architecture many times. Most authors use it in educational articles and YouTube videos due to its simplicity. There is no specific standard for classical architecture. However, often you can see the following format: The classic architecture has noticeable drawbacks. The biggest one is that the project becomes difficult to maintain due to implicit connections between components and module clutter. The drawbacks of the classic architecture become more apparent over time. The longer the project evolves, the more the application architecture becomes a tangled mess that is difficult to unravel. The classic architecture is suitable for small projects without ongoing maintenance or pet projects. Feature-Sliced Design, thanks to its concepts and standards, prevents the problems of classic architecture. However, the level of understanding and skills of developers working with FSD should be higher than when working with classic architecture. Usually, developers with less than 2 years of experience have not heard of FSD. However, when working with Feature-Sliced Design, problems need to be addressed "now" rather than "later." Issues in the code and deviations from the concepts become immediately apparent Comparison With Simple Modular Architecture Simple modular architecture has several drawbacks: Sometimes it is unclear where to put functionality into modules or components. Difficulties in using modules within another module. Issues with storing business entities. Implicit dependencies in global functions, leading to a tangled structure. It seems that in any complex or moderately complex projects, Feature-Sliced Design should be preferred over simple modular architecture. FSD solves many fundamental architectural problems and has few drawbacks. In terms of simplicity and development speed, a simple modular architecture may have an advantage over FSD. If an MVP is needed or a short-lived project is being developed, a simple modular architecture may be more suitable than FSD. But in any other case, a feature-sliced design looks preferable. The Potential of Feature-Sliced Design FSD is a young architectural methodology. However, it is already being used by many banking, fintech, B2B, e-commerce companies and others. Here is a link to the GitHub issue with a list of companies: . GitHub Issue The GitHub repository with the official FSD documentation had more than 1.1k stars at the time of publishing this article. The documentation is actively being expanded, and the FSD development team and community in Telegram and Discord are available 24/7 to help people with architecture-related questions. The potential of this architecture is highly regarded, and its usage is widely spread among large companies worldwide. With proper adoption, FSD has the potential to become the dominant architectural solution in the field of frontend development. Advantages and Disadvantages of Architecture Advantages Architecture components can be easily replaced, added, or removed Standardization of architecture Scalability The methodology is independent of the development stack. Controlled and explicit connections between modules without unexpected side effects. Business-oriented architectural methodology. Disadvantages A higher entry barrier compared to many other architectural solutions. Requires awareness, team culture, and adherence to concepts. Challenges and issues need to be addressed immediately, rather than later. Code problems and deviations from concepts are immediately visible. However, this can also be seen as an advantage. Conclusion Feature-Sliced Design is an interesting and valuable discovery that frontend developers should know and be able to use. FSD can provide teams with a flexible, standardized, and scalable architecture and development culture. However, utilizing the positive aspects of the methodology requires knowledge, awareness, and discipline within the team. FSD stands out among other architectures due to its clear business orientation, entity definition, functional composition, and components composition of the application. You can also independently explore examples of FSD usage in projects and the official Feature-Sliced Design documentation: Official Documentation Example. GitHub Client Example. Nike Sneaker and Footwear Store Example. Sudoku This post may be long, but I hope you have learned something new. I appreciate that you have finished reading this post. If you have any thoughts or questions, feel free to leave a comment! Also published here

The Best Complex Frontend Architecture: What You Need to Know About Feature-Sliced Design

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