Think Forward.

Ahmed Faras: The Eternal Legend of Moroccan Football 21414

Aziz Daouda, 9 mon.
- Society - Sports -
3.3 min read.
I have been fortunate enough to know Ahmed Faras. It is unbearable for me to speak of him in the past tense, someone who has been part of my life for so long. It had been ages since he last touched a ball. Few are still alive who saw him play, those who, match after match, would await his dribble, his runs down the wing, his shot, his goal. Faras was an outstanding man, with an incredible shyness and reserve. Even when present somewhere, he was always on the sidelines: discreet, courteous, kind, with deep sensitivity, affection, and great touchiness. But Faras will always be part of the present. He is a true legend of Moroccan and African football; legends never die. Fedala saw him born in the cold of December 1947. Mohammedia would be his city and Chabab his eternal club. At the time, there was no such thing as a transfer market, no migrations, no football mercenary spirit. You were born in a club, learned to play there, and you stayed. His temperament was not that of a typical striker: there was no aggressiveness, no cunning. He compensated with his genius and never needed to dive or roll on the ground to sway a referee or create confusion. His genius spared him all that. He was an exceptional striker who marked the history of Moroccan and continental football. The turf at El Bachir football stadium helped him, at that time, it was the best in Morocco. Ahmed Faras was the product of a generation shaped by the structured environment of the youth sports schools run by the Ministry of Youth and Sports, a system supposedly dismantled by so-called administrative and political reforms. Yet, it was there that Morocco's champions were formed, across all sports. His early path was marked by the guidance of renowned trainers such as Lakhmiri, who helped shape numerous Moroccan talents. This solid foundation allowed him to develop technical skills and a sense of teamwork very early on, which would become hallmarks of his play. Ahmed Faras spent his entire career at Chabab Mohammedia, from 1965 to 1982, never having a professional contract—such things didn’t exist in Morocco then. There’s no need to mention signing bonuses or performance awards, even with the national team. His loyalty to Chabab is remarkable. He would lead the club to a Moroccan championship and become its top scorer. He would bring along with him his playing friends—Acila, Glaoua, Haddadi, and many more. Faras was a pillar of the Moroccan national team. With 36 goals in 94 caps, what a historic scorer for the Atlas Lions! He captained the national team for eight years, playing in the 1970 World Cup in Mexico and the 1972 Munich Olympic Games. In 1975, Ahmed Faras entered the legend by becoming the first Moroccan to win the African Ballon d’Or, an award that underlined the quality and consistency of his play. This distinction placed him among the greatest players on the continent, competing with the top African stars of his era. There was talk of a transfer to Real Madrid...but at the time Moroccan league players were barred from moving abroad under penalty of losing their place in the national team. The idea was, thus, to strengthen the domestic league... The peak of his career was surely the 1976 Africa Cup of Nations (CAN), won by Morocco in Ethiopia. Faras was the leader on the pitch, the tournament’s top scorer, and his influence was decisive for this historic triumph—the only major African title that Morocco has ever won. He scored crucial goals against Nigeria and Egypt in that tournament, perfectly embodying the role of playmaker and team leader on the field. To this day, he remains the only Moroccan captain ever to lift the coveted African trophy. I have been a few times to that ground in Addis Ababa where he lifted the trophy, and every time, his image dominates my thoughts. An indelible black-and-white, forever etched in the history of the Kingdom and in the memory of Moroccans who followed the match at the time through the voice of one Ahmed Elgharbi...no live broadcasts back then. He was a respected and heeded captain, guided by great coaches: Abdelkader Lakhmiri, Blagoe Vidinic, Abdellah Settati, Jabrane, and especially Gheorghe Mardarescu during that epic campaign in the land of Emperor Haile Selassie. His charisma and vision of the game were crucial in unifying the team and leading them to the summit of African football. Faras embodied the spirit of conquest and national pride throughout the tournament. The squad was selected and led by an outstanding manager as well Colonel Mehdi Belmejdoub. His name is forever bound to that legendary achievement, a symbol of the potential of Moroccan football when guided by exemplary leadership, committed and knowledgeable managers, and players who were true warriors for their jersey’s colors. Ahmed Faras was not just a talented player. After his retirement, he continued to share his passion, getting involved in youth training, passing on his knowledge and love for the game to the new generation. He has been a source of inspiration for so many generations of players. Knowing Lhaj Ahmed Faras meant knowing a symbol of loyalty, talent, and unique leadership in Moroccan sports. His name will forever remain inscribed in collective memory as that of a football giant, whose legacy goes beyond sport to inspire entire generations. Rest in peace, my friend. One day, a great football stadium in this country will bear your name, and it will be fitting, if the players follow your example, honor your career, and if the public rises to your greatness, paying tribute to your distinguished name. So Lhaj Ahmed Faras, if you ever meet Acila up there, ask him to give you another nice pass, and tell Glaoua to defend well... Know that your star shines and will always shine above us in the sky of the beautiful country you cherished so much. ---
Aziz Daouda Aziz Daouda

Aziz Daouda

Directeur Technique et du Développement de la Confédération Africaine d'Athlétisme. Passionné du Maroc, passionné d'Afrique. Concerné par ce qui se passe, formulant mon point de vue quand j'en ai un. Humaniste, j'essaye de l'être, humain je veux l'être. Mon histoire est intimement liée à l'athlétisme marocain et mondial. J'ai eu le privilège de participer à la gloire de mon pays .


9600

33.0

Chapter 5: Formalize & Systemize 315

Satta Abraham, 1 d.
A working implementation begins with a narrowly defined document type. The unit of construction is a skill, which combines input schema, feature computation, semantic rules, generation constraints, and validation logic into a single packaged pipeline. The input schema defines the structure of accepted data. Each field has a fixed type and meaning. Inputs outside this structure are rejected or normalized before processing. This step removes ambiguity at the entry point. The feature layer computes derived values from the input schema. These computations are deterministic and expressed in standard tooling such as SQL or Python. The outputs include numerical transformations, aggregations, and formatted representations. Once computed, these values are stored and reused across all downstream operations for the same input. The semantic layer maps computed features into categorical labels. These mappings are expressed as explicit rules that define thresholds and conditions. The rules function as a translation layer between raw computation and narrative intent. Changes in business definition are reflected by modifying rules rather than rewriting logic. The generation layer receives three inputs: original data, computed features, and semantic labels. It produces structured text under strict constraints. The model is restricted to expressing provided values. No additional facts are introduced. Output formats are predefined, often as structured JSON containing narrative sections. The validation layer compares generated text against deterministic outputs. It extracts numerical values, categorical claims, and references, then checks them against the feature and semantic layers. Any deviation indicates failure. Output is either accepted or routed for correction. A complete skill behaves like a compiled artifact. Input enters through a fixed interface. Output is produced in a predictable format. Internal logic remains inspectable and versioned. Once a single skill is stable, the same structure can be replicated across multiple document types. Financial reports, product summaries, operational dashboards, and compliance documents follow identical architectural patterns. Variation exists only in schema definitions, feature logic, and semantic rules. As the number of skills increases, duplication appears in semantic definitions. Terms such as “strong performance,” “declining trend,” or “high risk” recur across domains, often with subtle differences in meaning depending on context. A static rule system cannot represent these contextual variations efficiently. Each skill encodes its own version of definitions, which leads to inconsistency and maintenance overhead. A knowledge graph introduces a shared semantic layer. Concepts are represented as nodes, and relationships between them are explicitly defined. Each concept carries attributes such as context, domain, and threshold values. This allows meaning to vary based on surrounding conditions rather than fixed rule files embedded in individual skills. In this structure, a query retrieves the appropriate definition of a concept based on context parameters such as industry, market state, or organizational role. The semantic layer no longer evaluates rules directly. It resolves references into context-specific definitions drawn from the graph. Feature computation remains unchanged. Inputs are still transformed into deterministic values. The difference lies in how those values are interpreted. Instead of fixed thresholds embedded in code or configuration files, interpretation depends on graph queries that return context-aware mappings. This creates composability across systems. Multiple skills reference the same underlying semantic nodes. A change in definition propagates through the graph without modifying individual pipelines. Consistency emerges from shared structure rather than replicated configuration. The generation layer remains unchanged. It still receives features and resolved semantic labels. The difference lies upstream, where those labels are derived from a shared semantic space rather than isolated rule sets. Validation also extends naturally. Outputs can be traced not only to feature computations but also to the specific semantic definitions used during interpretation. This adds a second layer of provenance, linking each statement to both numerical derivation and contextual meaning. The system shifts from isolated pipelines to a connected network of shared meaning, where document generation becomes an application of structured knowledge rather than repeated local interpretation.

Chapter 4: Tokenomics & Failure 318

Satta Abraham, 1 d.
Token usage in direct generation scales with both input size and document count. When identical datasets are used repeatedly, the same information is reintroduced into prompts and reprocessed each time. This creates redundancy across runs. A staged pipeline changes this behavior by separating computation from generation. Feature computation runs once per dataset. The results are stored and reused. The generation step receives only derived values and semantic tags rather than raw input data. Let Tin represent the original input size and T'in the reduced representation produced after feature extraction. For n documents derived from the same dataset, direct generation cost scales with n⋅Tin. In the staged system, cost splits into a one-time computation cost plus n⋅Tin. As n increases, the amortized cost of preprocessing becomes negligible relative to repeated generation savings. This structure also changes verification cost. When outputs depend on raw inputs embedded inside prompts, validation requires rechecking both computation and interpretation. When outputs depend on precomputed features, verification reduces to checking alignment between text and deterministic values. This reduces the scope of manual review. A second effect concerns failure containment. In end-to-end generation, errors in reasoning, calculation, and phrasing occur in the same process, making attribution difficult. A staged pipeline isolates these responsibilities. Feature computation is deterministic and testable. Semantic classification is rule-based and auditable. Generation is constrained to express only pre-validated inputs. Validation operates as a final comparison layer between text and deterministic outputs. In practical terms, this structure prevents entire classes of errors that arise when models are allowed to both compute and express facts. Numerical inconsistencies, misapplied rules, and unsupported claims can be traced back to specific layers and eliminated without affecting unrelated parts of the system. The result is a system where cost and correctness are both controlled through separation of responsibilities rather than increased model complexity.

Chapter 3: Prior Art and Pipeline Structure 322

Satta Abraham, 1 d.
The problem of translating structured input into structured output has been addressed in other domains through staged processing. Compiler design separates parsing, semantic analysis, transformation, and code generation into distinct phases, each operating on well-defined representations. Natural language generation research formalized a similar sequence, separating content selection, organization, lexical choice, and surface realization. These designs isolate responsibilities and prevent later stages from altering the assumptions established earlier in the pipeline. End-to-end neural generation replaced these staged systems with a single model that maps input directly to output. This removes explicit intermediate representations and shifts all responsibilities into one probabilistic process. While this simplifies implementation, it removes the boundaries that make verification and auditing feasible. When a model both computes values and expresses them, there is no clear point at which correctness can be enforced. A staged approach restores those boundaries. Data is transformed into a set of derived values using deterministic computation. These values are then mapped to semantic categories using explicit rules. Only after these steps are complete is text generated, and the generation step is constrained to use the prepared inputs. A final validation stage compares the generated text against the deterministic outputs to detect discrepancies. This structure ensures that computation, classification, and expression are handled independently. The model is not responsible for deriving facts, only for expressing them. Each stage produces artifacts that can be inspected, tested, and reused. The framework operates as a directed sequence of transformations from input data to validated text. Each layer has a defined input and output, and data flows forward without feedback into earlier stages. The input layer accepts structured records or extracts them from unstructured sources into a predefined schema. When extraction is required, it is limited to identifying and normalizing explicit facts without inference or aggregation. The goal is to produce a stable, typed representation of the data that downstream stages can consume. The feature layer performs deterministic computation. This includes arithmetic operations, aggregations, formatting, and lookups. The implementation can use SQL, Python, or any environment that produces consistent outputs for identical inputs. Results from this layer are cacheable and reusable, since they depend only on the input data. The semantic layer applies rule-based classification to the computed features. Rules encode domain definitions such as thresholds, categories, or states. These rules are externalized as data so they can be modified without changing application code. The output of this layer is a set of labels or tags that describe the state of the input according to business logic. The generation layer receives the original inputs, computed features, and semantic tags. The prompt specifies exactly which values must be included and prohibits the introduction of additional facts. Structured output constraints restrict the format of the response. The model converts the provided values into text without performing new calculations or introducing new data. The validation layer inspects the generated text and compares it against the outputs of the feature and semantic layers. Numeric values, percentages, and categorical statements are extracted and checked for agreement. Any mismatch results in rejection or routing to review. No document proceeds without passing this reconciliation step. This sequence enforces separation between computation, interpretation, and expression. It also creates a complete lineage from each statement in the text back to a deterministic source.

Chapter 2: Why Agents, MCP, and RAG Fail for Data-to-Text 322

Satta Abraham, 1 d.
The current default approach to generating documents from data combines agents, multi-step prompting, and retrieval. These methods are often grouped together in practice, but they introduce the same structural issue: the model repeatedly interprets and transforms the same data without a fixed, verifiable intermediate state. Start with agent workflows. A typical setup assigns roles such as writer, reviewer, and editor. Each role operates on text produced by the previous step while also referencing the original data. The data is not processed once and stored as a stable representation; it is re-read and reinterpreted at every stage. Derived values are recomputed multiple times, sometimes with small differences. The final document depends on a chain of generated text rather than a single transformation from source data. When a number is incorrect, there is no clear point in the process where the error can be isolated, because each stage mixes interpretation with generation. Multi-chain prompting attempts to impose order by splitting the task into explicit steps within a single workflow. One step extracts information, another computes metrics, another organizes structure, and a final step generates the document. This looks closer to a pipeline, but the boundaries are not enforced. Each step still depends on the model to preserve exact values from the previous step. Intermediate outputs remain probabilistic. A value that is slightly altered during extraction will be used as input for all subsequent steps. The system accumulates small inconsistencies rather than preventing them. Retrieval-augmented generation changes how data is accessed, not how it is processed. Relevant documents or records are retrieved and inserted into the prompt. The model then reads and synthesizes them. For data-to-text tasks, this means that the model is responsible for selecting, combining, and expressing values from retrieved sources. If multiple sources contain overlapping or conflicting information, the model resolves them implicitly during generation. There is no requirement that the output match any single source exactly. Retrieval improves coverage but does not enforce consistency. These methods are often combined. A system may retrieve data, process it through multiple prompting steps, and coordinate the process with agents. The number of transformations applied to the same data increases. Each transformation introduces another opportunity for deviation. Token usage grows because the same information is processed repeatedly. The final output reflects a sequence of interpretations rather than a controlled mapping from input to output. Data-to-text generation requires a different structure. Numerical values must remain exact. Classifications must follow defined rules. Every statement must be traceable to a source. These requirements assume that data is processed once, stored in a stable form, and then used consistently throughout the pipeline. Agents, MCP, and RAG do not provide this property because they rely on iterative interpretation. They remain useful in earlier stages where the goal is to gather information, explore alternatives, or synthesize unstructured inputs. In those contexts, variation is acceptable and often necessary. Once the data is fixed and the task is to produce a document that must align exactly with that data, the process must shift to a deterministic pipeline where computation, classification, and generation are separated and verified.
bluwr.com/Chapter 2: Why Agents,...

Chapter 1: Setting The Stage- Deloitte AI Scandal 322

Satta Abraham, 1 d.
In December 2024, the Australian government paid Deloitte $290,000 for a report that appeared complete and professionally written but contained fabricated material throughout. Several citations referred to sources that do not exist, some quotations were attributed to judges who never made them, and multiple references pointed to academic work that cannot be found in any database. The content was generated using GPT-4o and delivered to the client without these issues being identified during internal review. The problems were later discovered by a university researcher after the report had already been submitted, which led Deloitte to issue a corrected version and return the final payment. The failure originates from how current systems handle data-to-text generation. A single prompt is expected to read structured data, compute derived values, apply classification logic, organize content, and produce readable prose while preserving exact numerical and factual accuracy. These steps require different forms of reasoning, yet they are executed inside one probabilistic generation process without separation or verification between them. The result is text that is coherent at the surface level but unreliable when examined against the underlying data. This becomes a scaling problem rather than a one-off mistake. When document production relies on this approach, teams must allocate time to verify outputs, reconcile inconsistencies, and correct numerical or factual errors. As volume increases, the cost of review grows in proportion, often offsetting the time saved during generation. Attempts to improve reliability by adding more prompts or introducing agent-based workflows tend to increase repetition of the same operations without establishing a stable mechanism for verification. The approach presented in this series replaces that structure with a defined pipeline in which data processing, classification, generation, and validation are separated into distinct stages. Each stage has a fixed role, and outputs from earlier stages are treated as immutable inputs for later ones. The model is limited to producing language from already verified inputs rather than participating in computation or decision-making about the data itself.

Renault Restructuring: Social Threat or Industrial Opportunity for Morocco? 351

Aziz Daouda, 1 d.
- Politics - Business -
2.4 min read.
Renault's announcement of a drastic reduction in the number of engineers fits into a global dynamic of transformation in the automotive sector. Cost pressures, the shift to electric vehicles, and the digitalization of industrial processes: these factors are pushing major manufacturers to overhaul their internal structures, particularly in engineering roles. This still amounts to nearly 25% in Renault's case. At this stage, nothing indicates that Moroccan sites, particularly the Renault Tanger plant and the Renault Casablanca plant (SOMACA), will be affected, but the hypothesis deserves serious consideration. Above all, it opens up a field of strategic reflection. What if this potential wave of released expertise represented a historic opportunity for Morocco? For several years, major automotive groups have been redirecting their investments toward high-value-added areas such as embedded software, artificial intelligence, and electric batteries. This shift mechanically reduces the need for generalist engineers while creating strong demand for specialized profiles. It's a true global transformation redefining engineering in this industry. Renault's strategic plan, particularly through its electric subsidiary Ampere, illustrates this evolution. It's not just about cutting headcounts, but redeploying skills. Morocco is no longer merely a low-cost assembly site. Over two decades, the Kingdom has built one of Africa's most performant automotive ecosystems. It has evolved from an industrial assembly workshop to an integrated platform with local integration rates exceeding 60% in certain segments, the presence of major global tier-one suppliers, competitive logistics infrastructure (Tanger Med Port), and targeted training through highly effective specialized institutes. Groups like Stellantis and Lear Corporation have strengthened this ecosystem, consolidating Morocco's position as a regional industrial hub. If workforce reductions were to impact Morocco, they would release highly qualified profiles such as process engineers, quality specialists, industrial logistics experts, and R&D applied managers. A true pool of underutilized engineers. This human capital, trained to international standards, represents a rare strategic resource. In many countries, such a concentration of skills would be immediately absorbed by a dense local industrial fabric. In Morocco, the challenge is precisely to create these outlets. The hypothesis of a Moroccan automotive brand then imposes itself, with a central point: why not turn this constraint into a lever for industrialization? Morocco today has several assets: A solvent domestic market. The Moroccan middle class, though under pressure, remains capable of supporting demand for affordable, robust vehicles adapted to local realities. A near-complete supply chain. Wiring harnesses, seats, plastic components, cabling, majority of constituent elements are already produced locally, and industrial legitimacy has been achieved. The "Made in Morocco" automotive label is no longer an abstraction. In this context, the emergence of a national brand, with models symbolically named Taroudante, Fassia, or Itto, is no longer utopian. Even if it poses several structuring challenges, such as access to financing (patient capital, sovereign or private), mastery of intellectual property, the ability to develop a competitive technical platform, and an export strategy. There are precedents from comparable emerging countries worth examining closely. Countries like these have succeeded in this gamble: Dacia in Romania, successfully relaunched (irony of history, under Renault's impetus), Tata Motors in India, or Proton in Malaysia. These examples show that a national automotive industry can emerge provided there is clear alignment between the state, private capital, and technical expertise. It's truly a matter of political and industrial will. The real question, therefore, is not technical, but strategic. Does Morocco wish to remain a performant link in a globalized value chain, or does it aspire to become a full-fledged player capable of designing, producing, and marketing its own vehicles? The answer requires a proactive industrial policy, incentives for innovation, mobilization of national capital, and above all, confidence in local skills. It's about transforming uncertainty into an ambitious national project. If Renault's restructurings were to affect Morocco, they would rightly be perceived as a social threat. But they could also become a founding moment. Because behind every potentially released engineer lies a brick of industrial sovereignty. Stacked together, these bricks can form a true edifice. Morocco today has a rare alignment: skills, infrastructure, market, international credibility. What it still lacks, perhaps, is the audacity to take the final step: moving from the world's factory to brand creator. And in a country where the collective imagination is powerful, it's no small thing to envision that one day, owning a car named Fassia, Hada, or Itto becomes more than a purchase, truly an act of adherence to a Moroccan national industrial project.